ICT 217033 WHERE D6.2 Version 1.0 Survey on localisation in communication networks

WHERE D6.2 Version 1.0

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ICT- 217033 WHERE

D6.2 Version 1.0 Survey on localisation in communication networks

Contractual Date of Delivery to the CEC: 31/10/2008

Actual Date of Delivery to the CEC: 4/1/2009

Editor: Sylvie MAYRARGUE

Author(s): Sylvie Mayrargue, Benoît Denis, Christian Mensing, Ronald Raulefs, Stephan Sand, Mohamed Laaraiedh , Nadine Chapalain, Igor Arambasic, Vladimir Savic, Ziming He, Jimmy J. Nielsen, Jonathan Rodriguez, Joaquim Bastos, Stavros Stavrou, Marios Raspopoulos, Konstantinos Papakonstantinou

Participant(s): CEA, DLR, UR1, ITE, UPM, UNIS, AAU, IT, SIGINT, EUR

Work package: WP6

Estimated person months: (resources spent on the deliverable)

Security: PU

Nature: R

Version: 1.0

Total number of pages: 42

Abstract: This deliverable presents a literature survey of localisation in communication networks. In a first part, use of communication networks for location estimation is presented. Two families of methods do exist: fingerprinting and geometrical techniques. Cooperative positioning is one possible enhancement. In a second part, use of location information to improve radio resource management, specifically handover decision and relay selection, is presented.

Keyword list: Location, localization, positioning, fingerprinting, geometrical, Ultra-Wide Band, Time-of-Arrival, Time Difference-of-Arrival, Angle-of-Arrival, radio signal strength, anchor, handovers, relay.

Disclaimer:


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Executive Summary This deliverable presents the actual status of mobile radio activities using localisation within Europe and world wide.

In the first part of this document, methods using communication networks for localisation are presented. They are split into two main categories: fingerprinting and geometrical methods.

Fingerprinting can be considered as a branch of machine-learning, since it consists of two phases: an offline training phase and an online localization phase. In the offline phase, some radio parameters are collected at some known locations within the area where localization will take place in the online phase. During the online phase, measurements of these same parameters are compared to those in the data base, hence enabling localization, via a variety of methods. Fingerprinting can be applied to cellular systems (GSM, UMTS) to WLANs, and to UltraWideBand (UWB) nodes.

Geometrical methods use geometrical information of some known transmitters, generally base stations, to deduce user location. Typical parameters are Time-of-Arrival (TOA), which measures propagation time between user and base station, Time-Difference-of-Arrival (TDOA), which measures propagation time difference between user and two base stations, and Angle-of-Arrival (AOA), which assumes that at least one base station is equipped with an antenna array, so that angle between antenna array and user to base station direction can be estimated. Several such measurements, when adequately combined, may provide user location. These methods are mostly applied to cellular systems (GSM, UMTS), and also to UWB.

Cooperative positioning means that in addition to (mostly geometrical type) measurements between user nodes and some reference (anchor) transmitter, use measurements between user nodes themselves. All measurement scan be centralized, partially centralized, or distributed. Nodes may also exchange information in a multi-hop fashion.

Whenever available, commercial applications are described.

In the second part of this document, use of location for communication is presented. Here, location means not only geographical coordinates, but also speed and direction. Location information may help hand over decisions. Anticipating mobile location can avoid handover towards a very small cell, while at high speed. It can also mitigate the so-called “ping-pong effect”, where shadowing is mistaken for mobile being at cell edge. Lastly, in a relay-enabled network, relay choice and routing information may benefit from location information.

This deliverable presents a thorough state-of-the art of the above topics.

.


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Authors Partner Name Phone / Fax / e-mail AAU Jimmy Nielsen Phone: +45 9940 9867 Fax: N/A e-mail: [email protected] CEA Sylvie Mayrargue Phone: +33 4 38 78 62 42 Fax: +33 4 38 78 51 59 e-mail: [email protected] Benoît Denis Phone: +33 4 38 78 18 21 Fax: +33 4 38 78 51 59 e-mail: [email protected] DLR Christian Mensing Phone: +49 8153 282878 Fax: +49 8153 281871 e-mail: [email protected] Ronald Raulefs Phone: +49 8153 282803 Fax: +49 8153 281871 e-mail: [email protected] Stephan Sand Phone: +49 8153 281464 Fax: +49 8153 281871 e-mail: [email protected] UPM Igor Arambasic Phone: +34 91549 5700 ext.4006 Fax: +34 91336 7350 e-mail: [email protected] Vladimir Savic Phone: +34 91549 5700 ext.4006 Fax: +34 91336 7350 e-mail: [email protected] UR1 Mohamed Laaraiedh Phone: +33 2 23 23 50 75 Fax: +33 2 23 23 56 16 e-mail: [email protected] UNIS ZiMing He Phone : +44 1483 683 601 Fax : +44 1483 686 011 Email : [email protected] IT Jonathan Rodriguez Phone: +351 234 377900 Fax: +351 234 377901 e-mail: [email protected] Joaquim Bastos Phone: +351 234 377900 Fax: +351 234 377901 e-mail: [email protected]


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SIGINT Stavros Stavrou Phone: +357 22 32 52 40 Fax: +357 22 32 52 41 e-mail: [email protected] Marios Raspopoulos Phone: +357 22 32 52 40 Fax: +357 22 32 52 41 e-mail: [email protected] EUR Konstantinos Papakonstantinou Phone: +33 4 9300 8206 Fax: +33 4 9300 8200 e-mail: [email protected] ITE Nadine Chapalain Phone: +33 2 23455840 Fax: N/A e-mail: [email protected]


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Table of Contents

List of Figures..................................................................................................... 7

1. Introduction ................................................................................................... 8

2. Communications networks based localisation techniques ..................... 8 2.1 Fingerprinting ...............................................................................................................................10

2.1.1 Fingerprinting for GSM.......................................................................................................11 2.1.2 Fingerprinting for WCDMA................................................................................................13 2.1.3 Fingerprinting for WLANs..................................................................................................13 2.1.4 Fingerprinting for UWB ......................................................................................................17 2.1.5 Fingerprinting for generic cellular networks- .....................................................................19

2.2 Geometrical techniques ................................................................................................................21 2.2.1 TOA......................................................................................................................................21 2.2.2 TDOA...................................................................................................................................22 2.2.3 AOA .....................................................................................................................................23 2.2.4 Location techniques for TOA, TDOA and AOA................................................................24 2.2.5 Combination of several geometrical methods.....................................................................24 2.2.6 Commercial Applications ....................................................................................................26

2.3 Cooperative Positioning ...............................................................................................................27 2.4 Others............................................................................................................................................32

3. Localisation for Communications............................................................. 32 3.1 Location based RRM....................................................................................................................33 3.2 Location based handover:.............................................................................................................33 3.3 Relay based cooperative communications ...................................................................................36

4. Conclusions ................................................................................................ 36

5. References................................................................................................... 37


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List of Acronyms and Abbreviations

Term Description AWGN Additive White Gaussian Noise AOA Angle of Arrival AP Access Point BS Base Station CIR Channel Impulse Response CRLB Cramer-Rao Lower Bound DB Data base DOA Direction-of-Arrival EKF Extended Kalman Filter GP Gaussian Process LMS Least Mean Square LMU Location Measurement Unit LOS Line-of-Sight ML Maximum Likelihood MS/MT Mobile Station/ Mobile Terminal NBP Non Parametric Belief Propagation NLOS Non Line-of-Sight NN Neural Network PDP Power Delay Profile PSDP Power Space Delay Profile RRM Radio resource Management RSSI Received Signal Strength Indication RTT Round-Trip Time SNR Signal to Noise Ratio TDOA Time Difference of Arrival TOA Time of Arrival UE User Equipment UWB Ultra Wide Band WLAN Wireless Local Area Network


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List of Figures Figure 1 TOA location of a mobile station (MS), using triangulation from 3 base stations (BS)...............22 Figure 2 TDOA location of a mobile station (MS), using 3 base stations (BS)...........................................23 Figure 3 AOA location of a mobile station (MS), using 3 base stations (BS) .............................................23 Figure 4 Solving geometrical ambiguities with cooperative positioning.....................................................27 Figure 5: Location based ISHO using the HIS..............................................................................................34

List of Tables Table 2.1 Current location sensing technologies according to [HB01]..........................................................9 Table 2.2 Overview of indoor positioning versus outdoor positioning by satellite [SC+05] ......................10 Table 2.3 Overview of standard versus non-standard outdoor cellular network positioning [SC+05] .......10


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1. Introduction

This deliverable presents the actual status of mobile radio activities using localisation within Europe and world wide.

In the first part of this document, methods using communication networks for localisation are presented. They are split into two main categories: fingerprinting and geometrical methods.

Fingerprinting can be considered as a branch of machine-learning, since it consists of two phases: an offline training phase and an online localization phase. In the offline phase, some radio parameters are collected at some known locations within the area where localization will take place in the online phase. During the online phase, measurements of these same parameters are compared to those in the data base, hence enabling localization, via a variety of methods. Fingerprinting can be applied to cellular systems (GSM, UMTS) to WLANs, and to UltraWideBand (UWB) nodes.

Geometrical methods use geometrical information of some known transmitters, generally base stations, to deduce user location. Typical parameters are Time-of-Arrival (TOA), which measures propagation time between user and base station, Time-Difference-of-Arrival (TDOA), which measures propagation time difference between user and two base stations, and Angle-of-Arrival (AOA), which assumes that at least one base station is equipped with an antenna array, so that angle between antenna array and user to base station direction can be estimated. Several such measurements, when adequately combined, may provide user location. These methods are mostly applied to cellular systems (GSM, UMTS), and also to UWB.

Cooperative positioning means that in addition to (mostly geometrical type) measurements between user nodes and some reference (anchor) transmitter, use measurements between user nodes themselves. All measurement scan be centralized, partially centralized, or distributed. Nodes may also exchange information in a multi-hop fashion.

Whenever available, commercial applications are described.

In the second part of this document, use of location for communication is presented. Here, location means not only geographical coordinates, but also speed and direction. Location information may help hand over decisions. Anticipating mobile location can avoid handover towards a very small cell, while at high speed. It can also mitigate the so-called “ping-pong effect”, where shadowing is mistaken for mobile being at cell edge. Lastly, in a relay-enabled network, relay choice may benefit from location information.

This deliverable presents a thorough state-of-the art of the above topics, as can be found in available literature.

2. Communications networks based localisation techniques The following two overview papers provide an introduction to communications network-based localisation techniques:

• [HB01] J. Hightower and G. Borriello, “Location Systems for Ubiquitous Computing”, IEEE Computer, August 2001.

Besides providing an introduction to localisation techniques, [HB01] also compares different location sensing technologies and their accuracies (cf. Table 2.1).


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Table 2.1 Current location sensing technologies according to [HB01]

• [SC+05] G. Sun, J. Chen, W. Guo, and K.J.R. Liu, “Signal Processing Techniques in Network-Aided Positioning: A Survey of State-of-The-Art Positioning Designs”, IEEE Signal Processing Magazine, July 2005.

The more recent paper [SC+05] surveys state-of-the-art positioning designs, focusing specifically on signal processing techniques in network-aided positioning. It serves as a tutorial for researchers and


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engineers interested in this rapidly growing field. It also provides new directions for future research for those who have been working in this field for many years. This article provides a high level overview on standard and non standard signal processing techniques of communications network based localisation techniques. Table 2.2 gives an overview of indoor positioning versus outdoor positioning by satellite. Table 2.3 compares standard versus non standard location techniques for cellular communications networks. Where available it also provides some accuracy on the different technologies.

Table 2.2 Overview of indoor positioning versus outdoor positioning by satellite [SC+05]

Table 2.3 Overview of standard versus non-standard outdoor cellular network positioning [SC+05]

[D07] Bruce Denby “Geolocalisation in Cellular Telephone Networks” (Paris 6, ESPCI) NATO Advanced Study Institute on Mining Massive Data Sets for Security September 10-21, 2007, Villa Cagnola, Gazzada, Italy

This presentation gives a thorough overview of all available localization approaches in the field of cellular phone networks.

2.1 Fingerprinting As described in [YYN08], fingerprint-based techniques consist of two phases: an offline training phase and an online localization phase. In the offline phase, a radio map is built by tabulating RSS measurements received from signal transmitters at predefined locations in the area of interest. These values comprise a radio map of the physical region, which is compiled into a deterministic or


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probabilistic model for online localization. In the online localization phase, the real-time RSS (Received Signal Strength) samples received from signal transmitters are used to search the radio map to estimate a user’s current location based on the learned model. In the offline phase, a learned location-estimation model is essentially a mapping function between the signal space and the location space. Deterministic techniques build such a mapping by simply storing the average RSS values at a collection of known locations, and use the nearest neighbour method to locate a client. Probabilistic techniques, on the other hand, construct the mapping by storing the RSS distributions as the content of a radio map. The distributions are then used in a maximum likelihood calculation for localization. With sufficient training data, probabilistic methods are typically more accurate than their deterministic counterparts by directly handling the uncertainty of RSS measurements. In addition, one can note that performances are affected by whether user motion is taken into account or not. Measurement history reflecting user motion can be processed by using Hidden Markov models, Kalman filtering or particle filtering. Radio maps time-variation between training and online phases is another important issue, taken into account by some of the methods presented here.

2.1.1 Fingerprinting for GSM

[OVL+05] V. Otsason, A. Varshavsky, A. LaMarca, and E. de Lara. “Accurate GSM indoor localization”. In the Seventh International Conference on Ubiquitous Computing (UbiComp 2005), September 2005.

[VCL+06] Alex Varshavsky, MikeY. Chen, Eyal de Lara, Jon Froehlich, Dirk Haehnel, Jeffrey

Hightower, Anthony LaMarca, Fred Potter, Timothy Sohn, Karen Tang, and Ian Smith “Are GSM phones THE solution for localization?” IEEE Workshop on Mobile Computing Systems and Applications (HotMobile 2006), Semiahmoo Resort, Washington, USA, 2006

The second paper is a follow-up of the first one. Details about experiments and location algorithms are given in the first paper. Results are given for indoor and outdoor cases. Fingerprinting is used for indoor and outdoor, and centroid for outdoor. The authors mention that they used GSM traces provided by a GSM modem. These traces include signal strength from the 6-7 strongest neighbouring cells. In addition, the modem provides 35 cells signal strength, but without associated cell Ids. According to [OVL+05], the authors used this second set of measurements as data base. For indoor experiments, results are compared to those obtained using fingerprinting with Wi-Fi. (no details are given on the Wi-Fi data base, though). GSM localization system achieves within-floor localization results comparable to 802.11. Indeed, with data base measurements 1.5m apart, median indoor localization error ranges from 2,5m to 4.5m according to building characteristics, in both cases. Moreover, the GSM system effectively differentiates between floors regardless of the building structures, achieving correct floor classifications between 89% and 97% of the time. This is not the case of Wi-Fi, at least in one of the three considered cases. For outdoor experiment, GSM fingerprinting achieves median error below 75m. The centroid algorithm performs worse, achieving a 213m median error Fingerprinting is based on call the K-nearest neighbours algorithm [BP00a]. (see 2.1.3)

[VLH+07] Alex Varshavsky, Eyal de Lara, Jeffrey Hightower, Anthony LaMarca, Veljo Otsason “GSM indoor localization” Pervasive and Mobile Computing 3 (2007) 698–720

[VLH+07] is a follow-up of [OVL+05], with additional measurements and a focus on correct floor identification in tall multi-floors buildings.


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GSM fingerprinting results show to be correct up to 51% correct floor classifications and 96% of correct classifications within 2 floors. One reason is that GSM phones do not necessarily camp on the same cell during the training and measurement phases respectively.

[CSC+06] Mike Y. Chen, Timothy Sohn, Dmitri Chmelev, Dirk Haehnel1, Jeffrey Hightower, Jeff Hughes, Anthony La Marca, Fred Potter, Ian Smith, Alex Varshavsky “Practical Metropolitan-Scale Positioning for GSM Phones” Ubicomp 2006, LNCS 4206, pp. 225 – 242, 2006.

This paper presents location methods using GSM signals. It compares the centroid algorithm, fingerprinting and a particle filter algorithm applied to a radio map calculated from a propagation model. Urban and residential environments are considered (outdoor only). Fingerprinting gives the best results with a median error value of respectively 94m and 277m in these two environments. An interesting point is the study of precision decrease when the training period is performed with a different terminal type (radio, antenna) from that used for the testing period. Fingerprinting is much affected (almost 300%!), seemingly because the hand-over process triggers at slightly different positions in the training/testing phases.

See also section 2.1.3.2 [FHF06] B.Ferris, D.Hähnel, D.Fox “Gaussian Processes for Signal Strength-Based Location Estimation” Proceedings of Robotics Science and Systems 2006

[TQK+06] Takenga, C.M.; Quan Wen; Kyamakya, K.; “On the Accuracy Improvement Issues in

GSM Location Fingerprinting” Vehicular Technology Conference, 2006. VTC-2006 Fall. 2006 IEEE 64thSept. 2006 Page(s):1 – 5

This paper examines GSM fingerprinting in a dense urban mobile environment. Different methods based on neural network, database correlation, dead reckoning and a tracking algorithm for mobile users are examined for the purpose of determining the most accurate method for positioning. Results suggest that the combination of the pre-processing of the received signal and the usage of the extended Kalman filter for the case of the neural network method provide the best results in the case of mobile users. The positioning error in this case lies mostly within 10ths of meters

[K-AK06] M. Khalaf-Allah, K. Kyamakya “Mobile Location in GSM Networks using Database Correlation with Bayesian Estimation” Proceedings of the 11th IEEE Symposium on Computers and Communications (ISCC'06)

Here, a probabilistic approach is applied to outdoor GSM localization. Rxlev, which is the received signal level measured by all GSM terminals, is used as fingerprint. Training data are obtained via a 3D radio propagation model. Cell Id is associated to RxLev at a given location, in order to reduce the search space, and to avoid ambiguities. Three slightly different criteria are presented, that all make use of a “belief” function, where each possible location is associated to a weight. Weights are Gaussian functions around the training Rxlev’s. Mean accuracy ranges from 194m to 248m according to which method is used.

[SP+08] Simic, M.; Pejovic, P “An Algorithm for Determining Mobile Station Location Based on Space Segmentation”.Communications Letters, IEEE Volume 12, Issue 7, July 2008 Page(s):499 – 501

The authors of this work try to determine the position of a mobile user in a GSM network by segmenting the GSM network into zones, utilising the nearest base stations as a selection criterion and by treating the mobile user location as a two dimensional random variable. Simulations suggest that accuracies in the range of a couple hundred meters can be achieved.

• [MXN+07] Mavrogeorgi, N. Xenou, K. Nikitopoulos, D. Popescu, I. Constantinou, P. Nat. Tech. Univ. of Athens, Athens; “Mobile Terminal Subarea Localisation Method in GSM Networks”,Personal, Indoor and Mobile Radio Communications, 2007. PIMRC 2007. 3-7 Sept. 2007, page(s): 1-5.

The proposed method in the above paper tries to determine the subarea where the mobile user is located and to reduce the required initialization measurements. The method does not require any special hardware but only the serving and neighbouring cell IDs, the signal strength, the Location Area Code and the


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Mobile Country Code. Under certain assumptions, the method can reach a success prediction rate of 96.6%.

2.1.2 Fingerprinting for WCDMA

[LRZ+06] M. Layh, U. Reiser, D. Zimmermann, F. Landstorfer “Positioning of Mobile Terminals based on Feature Extraction from Channel Impulse Responses” IEEE Vehicular Technology Conference-Spring 2006

The authors suggest taking advantage of the channel impulse response (CIR) availability in 3G networks. So, instead of the received power level alone, CIR characteristics can be used as parameters for fingerprinting. In fact, only some extracted features from the Power Delay Profile are used, such as delay of the first tap, mean delay, delay spread, etc….Several ways of combining these features are compared. The authors propose to use propagation models instead of measurement campaigns. They provide a “virtual” test of their method in both suburban and urban environments, with Rake receiver CIR estimations simulated. Only two base stations are used. Accuracy is as low as 33;6 m (resp. 38.6m) in an urban (resp. suburban) environment.

2.1.3 Fingerprinting for WLANs

2.1.3.1 Nearest neighbour approach [BP00a] P. Bahl and V. N. Padmanabhan. RADAR: An in-building RF-based user location and

tracking system. In INFOCOM, pages 775–784, 2000 The authors use finger printing with Wi-Fi. They use RSS, rather than SNR, which are less stable. They had 3 base stations. In the training phase, they recorded not only the exact user location, but also his orientation, in order to take into account the user’s body effect. Then, they used a nearest neighbour approach, improved by a K-nearest neighbours algorithm, which averages the location of the K closest neighbours (or training points) in the radio map. For K=1, the median error value is under 3m. For K=5, it decreases to 2.75m. Without body effect, these figures drop resp. to 2.67m and 2.13m. With the worst case, where the off-line data set only has points corresponding to a particular orientation (e.g. north) while the real-time samples correspond to the opposite orientation (i.e. south), the median error value increases to 4.9m An alternative method to measurement campaign is proposed, using an indoor propagation model. In this latter case, median error value is as high as 4m.

[BP00b] P. Bahl and V. N. Padmanabhan “Enhancements to the RADAR User Location and Tracking System” February 2000 Technical Report MSR-TR-2000-12 Microsoft Research

In this follow-up paper, the authors use a new test bed, in addition to the previous one. The new test bed has 5 Access Points. The authors show that there is little benefit going beyond 3 APs. They also introduce a Viterbi-like algorithm in order to track a user location. The K nearest neighbour approach is still used, at each location sample, but in addition, the shortest path is calculated on a constant sliding time window h. User location is thus estimated with a constant lag h, as being the starting point of the shortest path. This avoids sudden “jumps” of the user between distant locations with similar radio characteristics. Performances for a user at walking speed show that this Viterbi-like algorithm out performs the simple independent successive location estimates of the same user locations. In addition, a short window length (6) is shown to be optimal. Another new feature introduced in this paper is to record and use multiple radio maps, taking into account environment variations and its impact on propagation. An example is an empty vs. crowded building. A calibration phase is based on self location of the AP using the RADAR algorithm. The radio map giving the most overall accurate location of the APs is deemed to be the correct one.


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2.1.3.2 Probabilistic approach

[RMT+02] Teemu Roos, Petri Myllymäki, Henry Tirri, Pauli Misikangas, Juha Sieva, “A Probabilistic Approach to WLAN User Location Estimation” International Journal of Wireless Information Networks, Vol. 9, No. 3, July 2002.

In this paper, the authors propose to use a probabilistic method instead of the nearest neighbour approach. The probabilistic approach consists in modelling the conditional probability distribution of the parameter observations obtained during the training phase, for a given location. This way, the distribution of received signal power at various locations is modelled. In addition, in contrast to nearest neighbour approach, uncertainty and errors in signal strength measurements are dealt with. Two models are proposed, a continuous one, based on a Gaussian kernel, and a discrete one, a histogram. Then, Bayes’ rule is used, in order to calculate the conditional probability of user’s location, given the observations. One possible optimization criterion is the minimization of the conditional expectation of the location, given the observations. This estimator tends asymptotically towards the 1-nearest neighbour when the Gaussian variance tends to 0. The authors provide a comparison between the two probabilistic approaches and the 1-nearest neighbour method, for indoor WLAN based positioning. The two probabilistic approaches give slightly better results than the 1-nearest neighbour method, in the case little or no averaging of past positions is done (up to 20 measurements are taken at the same location, so that averaging on part of these data simulates taking “history” into account). Otherwise, results are very similar. This sounds intuitively obvious, given that the probabilistic method has a smoothing effect on the observations, and so is the averaging process.

[YAS03] M. Youssef, A. Agrawala, U. Shankar, “WLAN location determination via clustering and probability distributions”, in: Proceedings of the First IEEE Conference on Pervasive Computing and Communications, 2003

This paper is very similar the previous one, except that the authors introduce a clustering of the parameters in the training phase. Basically, this clustering amounts to a grouping of locations corresponding to the same access points. Thus, search space size is reduced, and computational cost also. A comparison with RADAR, gives a much better accuracy for the clustering method.

[LBR+02] A.M.Ladd, K.E.Bekris, A.Rudys, G.Marceau, L.E.Kavraki, D.S.Wallach « Robotics-Based Location Sensing using Wireless Ethernet » In Proceedings of the Eighth Annual International Conference on Mobile Computing and Networking (MOBICOM), Atlanta, GA, Sept. 2002

This paper uses a probabilistic approach for indoor localization based on WLAN’s IEEE 802.11b signal levels. It has been adapted from robotics-based localization standard approaches. Similar to [BP00a], the authors noted a strong influence of user orientation on signal levels, and above all on signal level distribution. The authors use a Bayesian approach similar to [RMT+02]. The difference is that instead of using a statistical model, training data distribution is used. In addition estimations are possibly refined, using successive measurements, given the fact that a user does not move very quickly. A Hidden Markov Model was superimposed to the state space of the Bayesian approach. For a static experiment, Bayesian inference produces an error within 1.5 meters with probability 0.77. Hidden Markov models did improve the accuracy, up to about 40%, except in hallways with an open end.

[HFL+04] A. Haeberlen, E. Flannery, A.M. Ladd, A. Rudys, D.S. Wallach, L.E. Kavraki, “Practical robust localization over large-scale 802.11 wireless networks”, in: Proceedings of the Tenth ACM International Conference on Mobile Computing and Networking, Philadelphia, PA, 2004.

This work is the continuation of [LBR+02]. However the method presented in this paper is a trade-off between precision and robustness. In fact, the goal is to avoid lengthy training period, and huge volume of measurements, due to a tight grid of training points. In contrast, the authors claim that a precision up to a room fills most of user needs in an indoor environment. As a consequence, the training period is shortened, compared to [LBR+02], and lasts only a few minutes per room. Gaussian modelling of training data and histogram are both used. In spite of the fact that Gaussian model is a simplification of reality,


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accuracy is the same for both methods, while both the number of requested training data and the storage space are much less with Gaussian model. In addition, the authors take into account the issue of variations between training and testing periods. These variations can be due to different hardware devices, or environment variations such as people absent or present inside a room, doors open or closed, etc…The paper shows that a data calibration based on a linear relation between training and test data could solve the problem.

[SGT+03] A. Schwaighofer, M. Grigoras¸ V. Tresp, Clemens Hoffmann “GPPS A Gaussian Process Positioning System for Cellular Networks” In Advance in Neural Information Processing Systems (NIPS), 2003

This paper deals with indoor localization. The authors claim that all previous approaches suffer from the same drawback: a poor modelling of areas with low number of measurements data. Instead, they propose to model signal strength using Gaussian Processes (GP). They consider calibration signal strength data for a given base station as being jointly Gaussian, with zero mean and covariance matrix whose coefficients are a function of the distance between corresponding locations. Here, this function is a Matern kernel function. The parameters of this kernel have to be learnt on calibration data. Then, the authors claim that the signal strength at any location is also Gaussian, with close-form solutions for mean and variance, involving calibration data covariance matrix. Once these Gaussian distributions mean and variance have been calculated at each location for each base station, a joint maximum likelihood algorithm can be applied in order to estimate user position, given received signal strength measurements from all base stations at this position. This method is evaluated on a DECT network in an indoor environment.

[FHF06] B.Ferris, D.Hähnel, D.Fox “Gaussian Processes for Signal Strength-Based Location

Estimation” Proceedings of Robotics Science and Systems 2006

This paper uses the same GP model as [SGT+03], except that the correlation between measurements taken at different locations is modelled by a Gaussian instead of a Matern kernel. Additional features include tracking via particle filtering. This Bayesian filtering is improved by a mixed graph/ free space environment representation, which models constraints such as being in a corridor or in an open space. This method was applied to indoor WiFi localization, and to GSM outdoor localization. For WiFi, average accuracy was found to be 2.12m. In addition, GP was able to accurately extrapolate the signal strength model into rooms for which no training data was available. For GSM, GP was compared to [BP00a]. GP accuracy slightly outperforms RADAR in residential and suburban areas, while RADAR slightly outperforms GP in downtown areas.

Median error in m. Downtown Residential SubUrban

RADAR 94 255 293 GP 128 208 236

The reason is that cell towers density is lower in residential and suburban areas than in downtown areas. GP is able to extrapolate signal strengths values even with a few training data, which is not the case for RADAR.

2.1.3.3 Comparative study

[ELM04] E. Elnahrawy, X.Y. Li, R. P. Martin “The Limits of Localization Using Signal Strength: A Comparative Study” IEEE 2004

In this paper, the authors compare several fingerprinting techniques, to finally show that whatever technique is used, the results in terms of accuracy are very similar. Results are given in a WLAN case, but the authors claim that their results are general. First of all, they divide the area of possible locations into “tiles” (smaller areas of equal size, which gives a lower accuracy limit). Observed data during the training phase are interpolated, so as to allocate a pseudo observation to each tile.

o Area-based algorithms - “simple point matching “, which selects the tiles where observations are above a

certain threshold, for all access points.


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- “area based probability”, is very similar to the approach of [RMT+02], where the observation is modelled by a Gaussian distribution, and Bayes’rule is used to maximize the conditional distribution of the tile, given the observations. In the present method, [RMT+02] is extended to select a set of tiles, within a confidence level a, which a parameter to be tuned.

- “Bayesian networks” are graphs, which model path loss between APs and (unknown) measurement location. Because there is no close-form solution for the user location distribution, a Markov Chain Monte Carlo simulation approach is used. The drawback of this method is to give a large number of discontinuous tiles.

o Point-based algorithm

- RADAR, with 1 and 2 nearest neighbours, - Probabilistic approach, such as [RMT+02], and a modified version returning the mid-

point of the two best locations In both cases, a 3rd variation uses an augmented fingerprint data base, obtained by interpolation. Area-based methods show a trade-off between precision and accuracy. Point-based methods give very similar results. The authors give a rationale for this latter fact, claiming that the Bayesian Network method has a fundamental uncertainty limitation, due to poor channel modelling, and that would explain the limits of any other method.

[LL05] T.-N. Lin and P.-C. Lin, “Performance Comparison of Indoor Positioning Techniques based on Location Fingerprinting in Wireless Networks”, in Proc. 2005 International Conference on Wireless Networks, Communications and Mobile Computing, vol. 2, pp. 1569—1574, 2005.

This contribution compares three RSSI-based fingerprinting techniques using, neural networks, K-nearest-neighbor, and a probabilistic approach, respectively. Two sets of measurement data have been generated. For the offline data set is obtained from measurement locations at a grid of 2 m by 2 m. The set of data for online use is obtained at a different time using a finer grid of 1 m by 1 m. The techniques are compared with respect to the positioning accuracy, computational complexity, robustness, and scalability. The K-nearest-neighbor technique was found to outperform the two other candidates in terms of accuracy, robustness, and scalability; but is, however, more computationally complex. 2.1.3.4 Applications (Commercial services) www.Skyhookwireless.com SkyhookWireless is based in Boston (US), and provides location-based services, taking advantage of existing deployed WiFi. The service, called WPS (Wi-Fi Positioning System), is based on a fingerprinting approach, using all available Wi-Fi access points. A massive data base has been developed by Skyhook (22 million AP early 2008), with a coverage area of 2,800 cities and towns worldwide The white paper [S08] “Wi-Fi Positioning System Accuracy, Availability, and Time To Fix Performance Technical » 2008, is available on Skyhook website and describes WPS performances in various environments, compared to GPS. A comparison with GPS accuracy was performed in three environments: urban canyon, metropolitan and residential (suburban). Urban canyon is of course the most difficult environment for GPS, and also the most favourable for fingerprinting, due to the high density of APs. WPS outperforms GPS, e.g. median accuracy is 10m for WPS, and 40m for GPS. In Paris metropolitan area, median performances are identical, about 20m. In residential area, number and density of APs is reduced, and Line of Sight situations for GPS are more frequent, resulting in a median accuracy of 20m for GPS, and 30m for WPS. WPS also estimates mobile velocity. In a mixed environment test drive, median speed error was +/- 3 mph. In an urban area, a WPS scan will acquire approximately 9 access points. http://www.ekahau.com (Complex Systems Computation Group at the University of Helsinki.)


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The technology uses specific Ekahau Wi-Fi tags, as well as Wi-Fi enabled devices. Several alternative location methods are provided. - One of them is quite similar to Skyhook, except that it works within a specific Wi-Fi network- e.g. that deployed by a company. It is client-based. The client has to connect to the Wi-Fi network in order to report the measurements it performs, viz. the scanned RSSI of neighbouring APs. Ekahau Positioning Engine computes its location. Accuracy is claimed to be 1-3m. - Another is infrastructure-based, and requires modifications of the 802.11 standard features at the APs. The tag saves battery life by transmitting only on-demand by the APs. APs then collect signal level. Location computation is done using signal levels at APs. This latter method is thus less precise than the first one, with an accuracy of 3-10m. 2.1.4 Fingerprinting for UWB

[NDA06] C. Nerguizian, C. Despins and S. Affes, “Geolocation in Mines With an Impulse Response Fingerprinting Technique and Neural Networks”, in IEEE Transactions on Wireless Communications, vol. 5, no. 3, 603—611, March 2006.

Geolocation in mine environments are considered. Geometric methods relying on TOA, AOA, and RSS fail in these rough environments. Thus in this environment fingerprinting techniques are favored. A fingerprinting method which relies on parameters of the channel impulse response is introduced. The considered parameters are: the mean excess delay; the rms delay spread; maximum excess delay; total received power; power and delay of the first path; and the total number of multipath components. Measurements with 200 MHz bandwidth at a carrier frequency of 2.4 GHz are used. A neural network based on the Multi-Layer Perceptron (MLP) technique is proposed. Results show that 90 % of the position estimates are within 2 m accuracy.

[TK1+07] A. Taok, N. Kandil, S. Affes, and S. Georges. “Fingerprinting Localization Using Ultra-Wideband and Neural Networks”. IEEE 2007

In this paper, authors presented a localization technique in mines based on fingerprinting technique and UWB CIR. Mines conditions make solutions based on TOA , AOA or even RSS subject to big errors. The proposed fingerprints are given by 3 parameters 1- the excess delay 2- the total multipath gain which is the sum of all the received power from different detected multipath components of the same signal) 3- the root mean square delay spread which is the square root of the second central moment of the power delay profile. It measures the effective duration of the channel impulse response. Database correlation is performed by Neural Network (NN) technique: this choice is motivated by the capability of NN to combine different techniques (1st parameter which is time related, 2nd which is power related). Errors obtained for up to 10 meters are in order of centimeters which falls within the expected performances of an UWB system. Nevertheless, this technique is applied in 2D scenario. In a 3D scenario it may require a higher complexity in NN architecture.

[GJD+07] T. Gigl, G.J.M. Janssen, V. Dizdarevi, K. Witrisal and Z. Irahhauten. “Analysis of a UWB Indoor Positioning System Based on Received Signal Strength”. WPNC’07, Hannover, Germany

This paper explores the possibility to design an UWB ranging and positioning system using the RSS. Due to the extremely large bandwidths, the effects of small scale fading are reduced to the level where the knowledge of a calibrated path loss model can be employed for accurate and reliable distance estimation. This paper tested different configurations of BS and show different achieved accuracies. Best achieved mean distance error was 103 cm and worst one was 112 cm.

• [ASW06] F. Althaus, C. Steiner, A.Wittneben, “UWB Geo-Regioning Algorithm and Performance” Workshop on Positioning, Navigation, and Communication, WPNC’06

In this paper, authors present an algorithm of geo-regioning for UWB indoor positioning. A measurement compaign was performed in a warehouse with 22 regions and 600 Channel Impulse Responses collected


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by region. Then, the geo-regioning algorithm is performed based on the average power delay profiles (APDP) of the regions. These APDPs were used as inputs (fingerprints) for the algorithm. Authors propose a MAP (Maximum a posteriori) based estimator to match measurements and fingerprints. Results : 1- MAP matching estimator performs equally well as ML matching estimator. Improvements can be shown for high variance ratio between regions at lower number of taps. 2- Decreasing the number of taps decrease performances especially for similar regions. A criterion for tap selection is performed based on SNR and Pairwise Error Probability. 3- An approximation of the pairwise error probability has been derived, which enables a thorough investigation of the geo-regioning performance by avoiding time consuming simulations.

[SAT+08] C. Steiner, F. Althaus, F. Troesch, and A. Wittneben. “Ultra-Wideband Geo Regioning: A Novel Clustering and Localization Technique”. EURASIP 2008

This work introduces UWB geo-regioning as a clustering and localization method based on channel impulse response (CIR) fingerprinting, develops a theoretical framework for performance analysis based on ML and probability models, and evaluates this approach by means of performance results based on measured CIRs. Complexity issues are discussed and performance dependencies on signal-to-noise ratio, a priori knowledge, observation window, and system bandwidth are investigated. The presented performance results reveal the superiority of the algorithms assuming correlated CIR taps over those assuming independent taps. This implies that, for UWB geo-regioning, the reflectors and scatterers are correlated, and the channel tap correlations are region dependent. The drawback of the correlation is the increased number of model parameters, which must be estimated from a priori data. Authors show that this number can be significantly reduced by adjusting system parameters like bandwidth and observation window.

• [MA07] W.Q.Malik, B.Allen “Wireless Sensor Positioning with UltraWideBand Fingerprinting”, The Second European Conference on Antennas and Propagation, 2007, EuCAP 2007.

In this paper, authors investigate the use of UWB signals for sensor location fingerprinting in indoor environment using CIR measurements. The impact of LOS and channel bandwidth is detailed. First, fingerprinting framework and principles are presented. Position estimator corresponds to the maximization of CIR cross correlation coefficients. Authors also propose to use channel frequency responses instead of CIR in order to evaluate the impact of bandwidth. Then, measurements are performed in office environments in FCC UWB bandwidth (3.1—10.6 GHz) with 1601 discreet frequencies over this band. Different situations of LOS and NLOS are investigated. Results: 1- Positioning accuracy is higher if the correlation threshold is chosen carefully. 2- Positioning accuracy is higher in NLOS than in LOS due to the more pronounced sidelobes in the second case. Performances of UWB fingerprinting improve under increased scattering. 3- Accuracy is comparable to that obtained by ToA-based positioning. Indeed, the ambiguity region has a radius of 2 cm for lower correlation threshold. 4- Positioning accuracy depends significantly on bandwidth. Increasing this bandwidth increases the robustness of the correlation measure and so the positioning accuracy. Thus, the UWB channel estimation can be relaxed and system complexity reduced.

[JDW+05] D. Jourdan, J.J. Deyst, M. Win and N. Roy, “Monte Carlo Localization in Dense Multipath Environments Using UWB Ranging”, in Proc. IEEE International Conference on Ultra-Wideband, pp. 314—319, 2005.

This contribution introduces tracking of a person or a vehicle inside a building by use of a particle filter (PF). The system relies on beacons with known positions emitting UWB signals. Additional directional information is obtained from an inertial measurement unit (IMU). Experimental results are presented for three variants of the PF. In the PFs the target is assumed to move with constant speed. The first PF relies on the IMU data only, whereas the second PF combines the IMU data and time-of-arrival (TOA) data obtained from the beacon signals. The third PF accounts for the IMU bias as well as the shift in TOA caused by the propagation through walls. The third PF is able to track the target with an rms error of about half a meter.


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Commercial application www.ubisense.net Ubisense provides a precise real-time location system based on UWB tags. Accuracy is as high as 15cm 3D. Tags transmit UWB pulses used to determine their location Sensors are deployed in a convenient way, similar to cells in a cellular network. These sensors receive the UWB transmitted pulses, and calculate the tag location. In fact, they can identify the shortest propagation path, and have antenna arrays so they can calculate AOA. One single sensor can thus calculate location, if an assumption is made on the z coordinate of the tag. Otherwise, with two sensors, 3D positioning becomes possible. If the two sensors are furthermore connected via a timing cable to enable time difference of arrival (TDoA) measurement, then 3D accuracy improves still further – up to 15cm.

2.1.5 Fingerprinting for generic cellular networks-

2.1.5.1 Datamining approach In fact, all the above approaches can be related to the general issues of machine learning, pattern recognition and datamining. Indeed, all these approach, similar to fingerprinting, use two step methods. The first step is a training phase, where parameters are collected and possibly classified, followed by a testing phase, where observed data are compared to the training data via some metrics. In contrast to the previous ones, the following references have been written by members of the data mining community.

[YYN05a] Jie Yin, Qiang Yang, and Lionel M. Ni, “Adaptive Temporal Radio Maps for Indoor Location Estimation » Proceedings of the 3rd IEEE Int’l Conf. on Pervasive Computing and Communications (PerCom 2005)

[YYN05b] Jie Yin, Qiang Yang, and Lionel M. Ni, “Learning Adaptive Temporal Radio Maps

for Signal-Strength-Based Location Estimation” IEEE Transactions on Mobile Computing Vol.7, N° 7, pp 869-883, July 2008.

[YYN05b] is the follow-up of [YYN05a] Most of the above fingerprint-based approaches are based on a common assumption that the radio map built in the offline phase does not change much later in the online phase. A major limitation with this assumption stems from the dynamic characteristics of signal propagation and the environment, where the RSS values measured in the online phase can significantly deviate from those stored in the radio map, thereby limiting the localization accuracy in practical location-estimation systems. This paper objective is to extend current indoor location estimation techniques to cope with the variations of the radio maps at different time periods. This extension would allow the radio map built at one time instant to be adaptable and usable for other time instants. [HFL+04] already addressed this issue, and modelled time variations by a linear relationship between training and test data. However, the assumption was that a unique relationship could be used on a wide environment. The authors here claim this assumption is not necessarily valid, and propose better alternatives. They propose to set up some reference points, and to determine a multiple regression model between the received signal at some grid points and that at the reference points. This multiple regression model is assumed to remain the same between the training and test phases. During the test phase, received signal at reference points together with linear regression model are used to update estimate received signal at grid points. Then, Maximum Likelihood method is used to locate the mobile user. Another solution is to use a model tree. This tree is built using dichotomy. At each step, the training data are split into two groups, according to a threshold. The threshold is chosen so that the variance is minimized. The dichotomy ends when the group size reaches a pre-defined minimum. A linear regression scheme is associated with each branch. So, the main difference with the previous method is that more than one regression scheme model the training data. An experimental comparison between Maximum Likelihood, multiple regression and model tree, shows that model tree gives (slightly) better results than multiple regression, and much better than maximum likelihood.


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[PKY+07] S.J. Pan, J.T.Kwok, Q.Yang, J.J.Pan., “Adaptive Localization in a Dynamic Wi-Fi Environment through Multi-View Learning,” Proc. 22nd AAAI Conf. Artificial Intelligence (AAAI 07), AAAI Press, 2007, pp. 1108–1113.

Similar to the two previous papers, this paper deals with RSS based location methods in indoor environment, and tackles the issue of RSS radio map variations between the off-line measurement phase and the on-line testing phase. The idea here is to cast fingerprinting into the framework of supervised learning: in the offline phase, a mapping between known locations RSS and unknown location RSS is learnt by solving a minimization problem in a Reproducing Kernel Hilbert space of functions. In other words, the issue is similar to [SGT+03], except that instead of using GP for modelling unknown locations RSS, the extrapolation is performed by a linear combination of kernel functions. The issue of radio map variations between offline and online phase is then viewed in a co-regularized framework, where two mappings are calculated, one for the offline period, one for the online period. This method needs (a few) reference points with known locations, during the online phase. Then, the authors use an extension of the Manifold Co-Regularization algorithm of [SNB05] in order to find these two mappings. The extension comes from the fact that the number of known location does not need to be the same during the two phases. Accuracy seems to be robust to the number of reference points, which are only 5.

• [NJS04] X.L. Nguyen, M. I. Jordan and B. Sinopoli “A kernel-based learning approach to ad hoc sensor network localization” Report No. UCB/CSD-04-1319 April 2004 Computer Science Division (EECS) University of California

The problem studied in this paper is that of determining the location of a (large) number of sensors of unknown location, based on the known location of a (small) number of base sensors. Position of a sensor of unknown location is calculated solely on the basis of the receive/transmit signals between pairs of sensors (those of known locations, considered as “base” stations, and those between the sensor at stake and the base stations). In this paper, the coarse-grained localization problem is posed as a discriminative classification problem that can be solved using tools from the statistical machine learning literature-in fact pattern recognition. Fine-grained localization is then achieved by a second application of the coarse-grained localization technique. This localization algorithm thus involves two phases: first, there is a training phase that chooses discriminate functions for classifying positions using arbitrarily constructed boundaries. This first phase would normally be performed off-line at the base stations. Once the training phase is completed, other location-unknown sensors can determine their own position locally. The discriminate function is found via a method called “support vector machine” (SVM), as a weighted sum of kernel functions, one for each sensor belonging to the base stations. Kernel function should define a semi definite positive matrix. In this context, they provide a measure of similarity between any pair of sensors locations. Simulation results show an accuracy only on the order of half the distance between the sensors.

[YPZ07] IEEE 2007 ICDM Contest : Q. Yang, S. J. Pan, and V. W. Zheng, “Estimating Location Using Wi-Fi” IEEE Intelligent Systems Vol. 23, No. 1 January/February 2008

This paper describes what is claimed to be the first attempt to compare in a fair way various fingerprinting methods based on RSS of WLANs. A data base was recorded, containing both a training phase and a test phase. A worldwide competition between teams took place, with two tasks to fulfil. The first task involved static data, i.e. offline and online phases were simultaneous, while the second task involved dynamic data, where online phase data were recorded some time later. Brief summaries of each winner techniques are given in the paper. It is quite difficult to thoroughly understand the various methods, du to the briefness of the summaries. The winner for task 1 (IBM Tokyo Lab) used a multi class version of the label propagation method, a semi supervised-learning approach. Then, Tokyo University, who came second, used a kind of nearest neighbour approach. TsingHua University, who came 3rd, used a k-nearest neighbour approach, coupled with the notion of shortest path: indeed, some of the data were known to belong to a trace (to have been collected along a certain route). The winner of Task2 was HeBei University with a Minkowski Distance and Nearest-Unlike-Neighbor Distance Method. Chinese Academy of Science came second, with (Laplacian Regularized Least Squares Regression), a semisupervised-learning method for classification. Then, the transfer between the two different phases is performed by Locally Linear Preserving. IBM Tokyo Lab came 3rd, using a


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dimensionality reduction approach, based on a Laplacian eigenmap, followed by a nearest-neighbour method.

2.1.5.2 Power-(Space-)Delay-Profile Fingerprinting • [TSR+06] M. Triki, D.T.M. Slock, V. Rigal, P. Francois, ”Mobile Terminal Positioning via

Power Delay Profile Fingerprinting: Reproducible Validation Simulations”, Vehicular Technology Conference (VTCfall), Sept. 2006.

The authors in this paper investigate an impulse response power delay profile (PDP) localisation technique. The technique is based on matching an estimated PDP with a memorized PDP map for a given cell. Furthermore they introduce the power space delay profile (PSDP) to include spatial information which becomes available when the Base Station is equipped with an antenna array. Different simulations are provided highlighting the positioning accuracy vs SNR for varying numbers of base stations. Results reveal that for relatively high SNRs (>25dB), the PSDP outperforms the PDP and its positioning accuracy is within 10ths of meters.

• [TS+07] M. Triki, D.T.M. Slock, “Mobile Localization For NLOS Propagation”, IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Athens, Greece, Sept. 2007.

The work presented in [TS+07] is an extension of the work in [TSR+06]. In this contribution the authors consider not only a deterministic PDP estimation technique but also a Bayesian one, which is applicable for the case when the propagation paths are not resolvable in delay. The Bayesian approach allows to work with estimated channel impulse responses directly (instead of requiring to estimate a PDP) and to account for their estimation errors. They further prove that local identifiability of the MT position can be achieved if at least two resolvable paths are available.

2.1.5.3 Urban Scenario with 2GHz and 5GHz:

• [JB+04] C.A. Jötten, P.W. Baier, M. Meurer, S. Heilmann, T. Weber, and J. Maurer, “Reduced complexity signature based mobile terminal location relying on the knowledge of directional channel impulse responses” Proceedings of the Vehicular Technology Conference (VTC) Fall, September 2004.

In [JB+04] the authors offer a solution to the huge amount of data that is required to store the required fingerprints in a data base. The individual channel impulse responses are a superposition of multiple single wave forms. They state that single dominant wave forms remain unaltered longer than the superposition of many including the less dominant ones. They demonstrate their algorithms in a practical environment with mixed results.

2.2 Geometrical techniques

Geometrical techniques include Time-of Arrival, (TOA), Time Difference-of-Arrival (TDOA), and Angle-of-Arrival (AOA) based methods. Those methods combine measurements from different BS in order to obtain an estimate of the MS location. 2.2.1 TOA In the TOA-based radiolocation technique, the distance between an MS and a BS is obtained by finding the one-way (or one-way and return) propagation time between that MS and that BS and multiplying it by the propagation velocity (i.e., speed of light). Geometrically, this provides a circle, centered at the BS, on which the MS must lie. By using multiple BSs, the location of the MS can be resolved by triangulation. The TOA technique requires accurate synchronisation between the BS and MS clocks so that the measurements are adequate for the actual distances. Many of the current standards only mandate tight timing synchronisation among BSs. In addition, the MS clock itself might have a drift which directly generates an error in the location estimate.


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Figure 1 TOA location of a mobile station (MS), using triangulation from 3 base stations (BS)

In practice, propagation time for GSM can be derived from a parameter called Timing Advance (TA). However, due to the GSM rather large pulse duration, the position resolution (uncertainty on circle radius) of TA is about 500m. This is way too large for practical purposes. For UMTS, propagation time can be derived from a parameter called Round Trip Time (RTT). Combined with UMTS shorter pulse duration (0.26ms), RTT leads to an uncertainty of 36m with ½ oversampling, making it useful for practical purposes. For triangulation purposes, the mobile should be connected to three base stations, which means that the UE (user Equipment) should be in soft handover with these three stations. This can only happen if these stations belong to the so-called UE Active Set, which contains all BS received above a certain signal to noise threshold. However, the probability to have one mobile in soft handover with three NodeBs (Base Stations) in three different locations (no softer handover) is low. If the active set does not contain enough NodeB, because the received power from other NodeB is too low, to force Soft handovers with NodeBs out of the active set would mean a change in standards. See [BL06]. We can note that a connection with 2 base stations might be enough. However, an ambiguity between the two circles intersection might arise. In this case, a third measurement such as TDOA or AOA could resolve this ambiguity.

2.2.2 TDOA The TDOA-based radiolocation technique relies on the measurement of the time difference of arrival of a signal sent by the UE and received by several BSs. The constant time difference between two BSs defines a hyperbola, with foci at the BSs, on which the MS must lie. The intersection of two hyperbolic loci will define the 2-dimensional position of the MS. An important issue for TDOA systems is that they are not affected by errors in the MS clock time as this latter cancels out when subtracting two TOA measurements. However the synchronisation of the BSs (or at least, the known of the time offset between BSs) is still needed. This may require additional equipments at the BS. These additional equipments are called LMUs (Location Measurement Units).


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Figure 2 TDOA location of a mobile station (MS), using 3 base stations (BS)

In UMTS, measurements are made by the UE on the Pilot Channel, the mobile being in connected mode UE measures the parameter called "SFN-SFN time difference of arrival type 2" between several NodeBs. This is the time elapsed between the reception of a Pilot Channel slot of a node B and of the Pilot Channel slot of an other Node B which is closest in time. In theory, measurements on 3 different Node Bs are enough to compute the mobile position. UE location is determined as the intersection of two hyperbolas, corresponding to 3 Node B’s. However, in some cases, it is possible that the 2 hyperbolas intersect in two locations. A third TDOA measurement, using measurements from a 4-th Node B, or measurement of a different type (RTT, AOA) may resolve ambiguity. Note that soft Handover is not necessary even if an RTT measurement is used, since at least one RTT should be available in the cell where the UE camps.

2.2.3 AOA AOA techniques assume that at least one BS is equipped with an antenna array, so that transmit and receive signals are beamformed.

Figure 3 AOA location of a mobile station (MS), using 3 base stations (BS)

As can be seen Figure 3, only 2 BS are enough for MS location. However an ambiguity region does exist on the line in between both NodeBs. Therefore a 3rd BS is preferred. An alternative is 2 BS plus a measurement of another type.

The AOA availability assumes that adaptive antennas are present at least one NodeB. For UMTS, availability of AOA measurements from several NodeB’s implies as in the TOA case that the UE is in soft handover with them. Then same remarks as above apply about the active set size.


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One AOA and one TOA or TDOA measurement can ensure location.

2.2.4 Location techniques for TOA, TDOA and AOA The MS location is determined once the distance estimations from the set of BSs are known. The location is obtained by finding the intersection of the circumferences (circles or spheres for TOA and hyperbolas or hyperboloids for TDOA), i.e. solving n non linear equations simultaneously, where n is the number of considered BSs. To obtain a precise position estimate at reasonable noise levels, the Taylor’s series method is commonly employed [F76]. It is an iterative method, which starts with an initial guess and improves the estimate at each step by determining the local Least Square (LS) solution. In practice the convergence of this method is not assured when the initial guess is not close to the exact position and it is computationally intensive. The non iterative Chan’s method for TOA [CYC03] and for TDOA [CH94] gives an explicit solution is considered to be more appropriate. It is an approximation of the Maximum Likelihood (ML) estimator and attains the theoretical error lower bound. The accuracy of radio location schemes depends on the propagation conditions of the wireless channels. If Line-of-Sight (LOS) propagation exists between MS and all BSs, high location accuracy can be achieved. However, in wireless communication systems in which direct path from the MS to a BS is blocked by buildings and other obstacles, signal measurements include an error due to the excess path length travelled because of reflection or diffraction, which is termed the Non Line-of-Sight (NLOS) error [WH96]. Range measurements are corrupted by standard measurement noise and NLOS errors. NLOS bias is relatively large and was reported to be quite common in all environments, except for rural areas [SR96]. Based on the assumption that the standard deviation of the NLOS range is greater than that for LOS measurements, this has led to the development of algorithms that focus on removing or mitigating the NLOS measurements errors such as [C99, WWO03, ACY04] for TOA, [CZ05] for TDOA and [X98] for AOA. Those algorithms consider that both LOS and NLOS links to the MS are included and cannot provide much improvement in location accuracy if the propagation at all BSs is NLOS. However in most general scenario and especially for macrocells, all BS can be expected to be NLOS. Hence, new algorithms based on the use of scattering models to classify propagation environments and utilize multipath measurements rather than only the earliest arriving multipath component in order to incorporate the NLOS effect into the location algorithm were developed [ACY07]. These algorithms were also shown to be robust when environments are incorrectly classified and when the classification model does not match perfectly the actual channel conditions. Position estimator performance can be measured by several parameters, namely: - Circular Error Probability (CEP) if an estimator is unbiased and describes the scattering of the position estimate around the true position of the MS. The CEP is the measure specified by the FCC. - Geometric Dilution of Precision (GDOP) is the standard deviation of the range measurements. - Root Mean Square Error (RMSE) is the square of the distance between a true mobile position and an estimated mobile-position. .Furthermore, every position estimator performance can be evaluated by comparing estimator’s RMSE to the Cramer-Rao Lower Bound (CRLB) [K04].

2.2.5 Combination of several geometrical methods In the following we present several papers that use combinations of several geometrical techniques. TOA(+AOA):

• [STK05] A. H. Sayed, A. Tarighat, and N. Khajehnouri, “Network-Based Wireless Location”, IEEE Signal Processing Magazine, July 2005.

This article provides an overview of wireless location challenges and techniques with a special focus on network-based technologies and applications. Benchmark are the FCC requirements and the difficulties intrinsic to the wireless environment that make meeting these requirements challenging; these challenges include channel fading, low signal-to-noise ratios (SNRs), multiuser interference, and multipath conditions. The focus is on a CDMA based communications system where especially TOA and AOA techniques were investigated.


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In the following, the main system parameters and simulation results in terms of achievable accuracies for different setups and algorithms are summarized:

• System parameters o CDMA cellular network o CDMA chip rate of 4 MHz o Processing gain of 64 o 3-tap Rayleigh fading channel with path loss exponent of two o Antenna array of size four at the BS o Doppler frequency corresponding to a maximum speed of 30 mph o Multi-user environment with a different number of active users

• TOA: 67% Outage=120 m • TOA: 95% Outage=330 m • TOA+AOA: 67% Outage=47 m • TOA+AOA: 95% Outage=144 m • TOA+AOA+Interference Cancellation: 67% Outage=45 m • TOA+AOA+Interference Cancellation: 95% Outage=128 m

RSS/TOA/TDOA/AOA:

• [GG05] F. Gustafsson and F. Gunnarsson, “Mobile Positioning Using Wireless Networks: Possibilities and Fundamental Limitations Based on Available Wireless Network Measurements”, IEEE Signal Processing Magazine, July 2005.

[GG05] discusses and illustrates possibilities and fundamental limitations associated with mobile positioning based on available wireless network measurements. This article does not address a specific communication technology, but instead compares in a generic way the performance of different measurement categories based on the CRLB or approximations of the CRLB. The article discusses RSS, TOA, TDOA, AOA, digital map information, and position estimates as measurement types. Specific issues on accuracy limitation in each measurement, such as synchronization and multipath problems, are only briefly commented upon. The paper not only considers static positioning solutions, but also dynamic positioning taking into account a mobility model and an adaptive filter. Mobility models addressed in the paper are random walk, velocity sensor, random force and inertial sensor models. The paper considers the LMS, RLS, EKF and PF adaptive filters. RSS/TOA/AOA:

• [PAK+05] N. Patwari, J.N. Ash, S. Kyperountas, A.O. Hero III, R.L. Moses, and N.S. Correal, “Locating the Nodes: Cooperative Localization in Wireless Sensor Networks”, IEEE Signal Processing Magazine, July 2005.

[PAK+05] presents an overview on cooperative localization in wireless sensor networks. This article describes measurement-based statistical models useful to describe TOA, AOA, and RSS measurements in wireless sensor networks. Wideband and UWB measurements, and RF and acoustic media are also discussed. Using the models, the authors show how to calculate a CRLB (Cramer-Rao Lower Bound) on the location estimation precision possible for a given set of measurements. A numerical example demonstrates how to compute the Cramer-Rao bound on the location RMSE for RSS, TOA, and AOA measurement with certain measurement variances. The results demonstrate that AOA outperforms TOA, which in turn outperforms RSS, for the given measurement uncertainties. Finally, the authors briefly survey sensor localization algorithms. Here, they differentiate between centralized and distributed algorithms. RSS/TOA/TDOA/AOA:

• [GTG+05] S. Gezici, Z. Tian, G.B. Giannakis, H. Kobayashi, A.F. Molisch, H.V. Poor, and Z. Sahinoglu, “Localization via Ultra-Wideband Radios: A Look at Positioning Aspects of Future Sensor Networks”, IEEE Signal Processing Magazine, July 2005.

[GTG+05] concentrates on positioning via UWB in sensors networks with the three techniques AOA, TOA, TDOA, and RSS. The paper provides CRLBs for RSS and TOA. Further, it discusses main sources


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of errors, such as multi path propagation, multiple access interference, NLOS propagation. Besides that, the paper also presents simulation results for the CRLB of a hybrid positioning with TOA and RSS measurements. CellID+TOA

• [BL06] J. Borkowski and J. Lempiainen, “Practical Network-Based Techniques for Mobile Positioning in UMTS”, EURASIP Journal on Applied Signal Processing, Volume 2006, DOI 10.1155/ASP/2006/12930.

This article presents results of research on network-based positioning for UMTS (universal mobile telecommunication system). Two new applicable network-based cellular location methods are proposed and assessed by field measurements and simulations. The obtained results indicate that estimation of the position at a sufficient accuracy for most of the location-based services does not have to involve significant changes in the terminals and in the network infrastructure. In particular, regular UMTS terminals can be used in the presented PCM (pilot correlation method), while the other proposed method - the Enhanced CID with RTT (cell identification + round trip time) requires only minor software updates in the network and user equipment. The performed field measurements of the PCM reveal that in an urban network, 67% of users can be located with an accuracy of 70 m. In turn, simulations of the ECID+RTT report accuracy of 60 m–100m for 67% of the location estimates in an urban scenario. In the following the main system parameters and simulation results in terms of achievable accuracies for different setups and algorithms are summarized:

• System parameters o CDMA cellular network o Equally spaced (1 km) 6-sectored sites in a hexagonal grid with constant antenna

directions o UMTS parameters o Urban and suburban simulations (different multipath models)

• Suburban: 67% Outage=50 m • Suburban: 95% Outage=140 m • Urban: 67% Outage=70 m • Urban: 95% Outage=170 m

Note that the PCM method belongs to the finger-printing family technique

2.2.6 Commercial Applications http://www.trueposition.com/

• [T08] “An Examination of U-TDOA and Other Wireless Location Technologies: Their Evolution and Impact on Today’s Wireless Market” White Paper

In 1996, the US Federal Communications Commission (FCC )issued a mandate that required all wireless operators to implement highly accurate systems to determine the location of all 911 (emergency) calls.. Requirements were further refined in the subsequent years, and are as follows. – for network based location: 67% of calls should have accuracy lower than 100m, 95% of calls an accuracy lower than 300m – for mobile based: 67% of calls should have accuracy lower than 50m, 95% of calls an accuracy lower

than 150m Once implemented, these requirements provided useful also for commercial applications development.

After several years of technical trials, market deployments, and subsequent failures, the major GSM providers in the US have widely adopted the network-based technology: U-TDOA. (U=Uplink). As explained above, U-TDOA requires that the base stations be synchronized (which is not normally the case in a GSM or UMTS-FDD network). Therefore, additional equipments called LMUs (Location Measurement Units) have to be installed at some specific BS. This method of location produces very accurate measurements, typically in the range of 50 meters. Of course, the accuracy increases with the number of installed LMUs. In normal practice, as many as 50 sites can be involved in a single location measurement. It should be noted that this technology provides accurate locations even when the mobile caller is inside a large building.


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The GSM standards bodies have defined an architecture to support the location of wireless phones and the U-TDOA method forms part of those standards. Key elements are - LMUs, which are more sensitive than BSs and can detect weaker signals, - and a Serving Mobile Location Center (SMLC), whose role is to manage LMUs, calculate positions, and interface with the network. U-TDOA systems typically deliver locations to the network in less than 10 seconds from the initiation of the call. They can even deliver a first estimate in 5s in emergency calls.

2.3 Cooperative Positioning Multiple targets/mobiles/blind nodes can cooperate by measuring ranges not only from the beacons/references/anchors/landmarks/BS, but also between each other. Multilateration algorithms can incorporate information gathered through cooperation accordingly, with the purpose of improving the position estimation accuracy and/or coverage. The gains from cooperation are:

- Solving out geometrical ambiguities - Enhancing estimation accuracy - Enhancing coverage by benefiting from

- Enhanced graph rigidity (e.g. if no sufficient anchors are available for one mobile, degrees for freedom on the position can be progressively removed)

- Information redundancy (e.g. more measurements are integrated to enrich the estimation problem)

- Spatial diversity (different “channels” are seen for each mobile node, and hence, with specific statistics for the errors affecting measured radiolocation metrics, opening the door to “selective cooperation” relying on the best links among possible links)

Figure 4 Solving geometrical ambiguities with cooperative positioning

« Cooperative » may also mean : « cooperation by information exchanges », e.g.

• « Two-Way ranging » protocols for packets time-of-flight measurements are often said to be cooperative when between asynchronous devices (« handshake » protocols).

• Distributed positioning estimation may rely on cooperative information exchanges between neighbours (e.g., nodes may exchange information that cannot be forwarded single-hop, but only multi-hop).

The following references give some illustrations of the above cases. .


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• [BL08] A. Baggio and K. Langendoen, “Monte-Carlo Localization for Mobile Wireless Sensor Networks”, Ad Hoc Networks, Delft University of Technology – The Netherlands, July 2008.

Range-free anchor-based localization algorithm is proposed for mobile wireless sensor networks using Monte-Carlo (MC) Boxed Localization algorithm. The localization accuracy is improved at a cost of communication traffic, since nodes exchange with each other information on the locations of anchor nodes. By cooperating with its neighbours, a target node can be aware of the anchors that are outside its radio range. This information is than used when constructing a box at the place where anchors' radio ranges overlap. The box is the region of the deployment area where the sensor node is localized. The algorithm proceeds in two steps: prediction and filtering. During the prediction step, a sensor node generates a new set of samples based on the previous set, and during the filtering phase the information obtained from the neighbouring nodes is used to remove the impossible locations from the new set of samples. When compared to conventional MC localization methods, the accuracy of boxed MC approach is improved by a minimum of 4% and by a maximum of 73% (average 30%), for varying node speeds when considering nodes with knowledge of at least three anchors. The coverage is strongly affected by speed and its improvement ranges from 3% to 55% (average 22%) and the processing time is reduced by 93% for similar localization accuracy.

• [HZ07] R. Huang and G. V. Zaruba, “Incorporating Data from Multiple Sensors for Localizing Nodes in Mobile Ad Hoc Networks”, Mobile Computing, IEEE Transactions on, September 2007.

In this paper, it is shown which type of sensor is better suited for which type of network scenario and how different sensors types could coexist in the same localization framework. One general particle-filtering framework is provided, the first of its kind that allows heterogeneity in the types of sensory data to solve the localization problem. When compared to localization scenarios where only one type of sensor is used, this framework provides significantly better localization results. Furthermore, this approach provides not only a location estimate for each non-anchor, but also an implicit confidence measure on how accurate this estimate is. This confidence measure enables nodes to further improve their location estimates using a local iterative one-hop simple message exchange, without having to rely on synchronized multiphase operations like in traditional multilateration methods.

• [VW07] V. Vivekanandan and V. W. S. Wong, “Concentric Anchor Beacon Localization Algorithm for Wireless Sensor Networks”, Vehicular Technology, IEEE Transactions on, September 2007.

A new localization algorithm denominated Concentric Anchor Beacon (CAB) is proposed for wireless sensor networks. CAB is a range-free approach and uses a small number of anchor nodes. Each anchor emits beacons at different power levels. From the information received by each beacon heard, nodes can determine in which annular ring they are located within each anchor. Each node uses the approximated center of intersection of the rings as its position estimate. Two heuristics are also proposed, namely CAB with Equal Area and CAB with Equal Width, to determine the transmitting power levels of the beacons. Simulation results show that the estimation error is reduced by half when anchors transmit beacons at two different power levels instead of at a single power level.

• [IF05] A. T. Ihler, J. W. Fisher, R. L. Moses and A. S. Willsky, “Nonparametric Belief Propagation for Self-Localization of Sensor Networks”, IEEE Journal on selected areas in communications, April 2005.

In this paper, self-localization of sensor networks involves the combination of absolute location information (e.g., from a global positioning system) with relative calibration information (e.g., distance measurements between sensors) over regions of the network. In general it is desirable to distribute the computational burden across the network and minimize the amount of inter-sensor communication. It is demonstrated that the information used for sensor localization is fundamentally local with regard to the network. The localization is done using nonparametric belief propagation (NBP) approach, a recent generalization of particle filtering, for both estimating sensor locations and representing location uncertainties. The advantage of NBP include easy implementation in a distributed fashion, admitting a wide variety of statistical models (it is not restricted to Gaussian measurement models), and possibility of representing multimodal uncertainty. Using simulations of small to moderately sized sensor networks, It is shown that


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NBP may be made robust to outlier measurement errors by a simple model augmentation, and that judicious message construction can result in better estimates. The authors also provide an analysis of NBP’s communications requirements, showing that typically only a few messages per sensor are required, and that even low bit-rate approximations of these messages can be used with little or no performance impact.

• [PAK+05] N. Patwari, J.N Ash, S. Kyperountas, A.O. Hero III, R.L. Moses and N.S. Correal, “Locating the nodes: cooperative localization in wireless sensor networks”, IEEE Signal Processing Magazine, July 2005.

This reference, already mentioned in 2.2.5, relates geometrical techniques to the issue of cooperative positioning.

• [LR03] K. Langendoen and N. Reijers, ”Distributed localization in wireless sensor networks: a quantitative comparison”, Computer Networks: The International Journal of Computer and Telecommunications Networking, November 2003.

A problem of determining the node locations in ad-hoc sensor networks is studied. Three distributed localization algorithms are compared (Ad-hoc positioning, Robust positioning, and N-hop multilateration) on a single simulation platform. The algorithms share a common, three-phase structure: (1) determine node–anchor distances, (2) compute node positions, and (3) optionally refine the positions through an iterative procedure. A detailed analysis is presented comparing the various alternatives for each phase, as well as a head-to-head comparison of the complete algorithms. The main conclusion is that no single algorithm performs best as the algorithm performance depends on the system conditions (range errors, connectivity, anchor fraction, etc.).

• [PB03] N. B. Priyantha, H. Balakrishnan, E. Demaine, and S. Teller, “Anchor-Free Distributed Localization in Sensor Networks”, MIT Laboratory for Computer Science, April 2003.

A fully decentralized algorithm called AFL (Anchor-Free Localization) is described. The algorithm starts from a random initial nodes positions and converges to a consistent solution using only local node interactions. The key idea in AFL is fold-freedom, where nodes are first configured into a topology that resembles a scaled and unfolded version of the true configuration, and then a force-based relaxation procedure is executed. The simulations under a variety of network sizes, node densities, and distance estimation errors, demonstrated that this algorithm is superior to conventional methods that incrementally compute the coordinates of nodes in the network. The superiority is seen mainly in terms of its ability to compute correct coordinates under a wider variety of conditions and its robustness to measurement errors.

• [NN03] D. Niculescu and B. Nath, “Ad Hoc Positioning System (APS) Using AOA”, IEEE INFOCOM 2003, April 2003.

In this paper, it is shown how angle-of-arrival (AOA) capability of the nodes can be used to derive position information. One method is proposed for all nodes to determine their orientation and position in an ad-hoc network where only a fraction of the nodes have positioning capabilities, under the assumption that each node has the AOA capability. Two algorithms are proposed DV-Bearing and DV-Radial, each providing different signaling-accuracy-coverage-capabilities trade-offs. The advantages of the method are that it provides absolute coordinates and absolute orientation, it also works well for disconnected networks, and doesn’t require any additional infrastructure. Simulations showed that resulted positions have accuracy comparable to the radio range between nodes, and resulted orientations are usable for navigational or tracking purposes.

• [KRS94] Kurazume, R. Nagata, S. Hirose, S, '' Cooperative Positioning with Multiple Robots,'' in Proc. IEEE International Conference on Robotics and Automation, vol.2 pp.1250-1257, May. 1994

This paper proposed a cooperative positioning scheme for robots. The robots are divided into two groups A and B. One group, say A, remains stationary and acts as a landmark while group B moves. The moving group B then stops and acts as a landmark for group A. The procedure is repeated until the target robot position is reached. In this way, the entire group of the multiple robots travels while maintaining knowledge of their positions.


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An example of three mobile robots was studied. The relative angles between each other are measured, and then a formula giving the relationship between the distance moved by each robot and the positioning error variance was defined. Also, a optimum condition to minimize positioning error was derived. Simulation was done to verify the theory.

• [SRB+01] C. Savarese, J.M. Rabaey, and J.Beutel, ''Location in distributed Ad-hoc wireless sensor networks,'' in Proc. IEEE ICASSP, May 2001, pp. 2037-2040.

This paper proposed a distributed positioning algorithm, every single node repeatedly and concurrently receive ranging and location information from neighbouring nodes, solve a local localization problem, then transmit the obtain results to the neighbouring nodes until convergence. Using this method, the global positioning challenge is translated into a number of distributed local optimization problems that iteratively converge to a global solution. The advantage of this approach is that no global resource and communications are needed. The disadvantage is that convergence may take some time and that nodes with high mobility may be hard to cover. Simulation results show that cooperative positioning, a combination of TERRAIN (Triangulation via Extended Range ad Redundant Association of Intermediate Nodes) and refinement algorithm, is capable of producing position estimates with errors as low as 5%.

• [NN01] D.Niculescu and B. Nath, ''Ad Hoc Positioning System (APS),'' in Proc. IEEE Globecom. vol.5, pp.201-212, Apr. 2001.

This paper proposed a distributed positioning algorithm, which does not require special infrastructure or setup, provides global coordinates and require re-computation only for moving nodes. It is a hop by hop algorithm, each sensor estimate its multihop range to the nearest reference nodes. These ranges can be estimated via the shortest path. When each sensor has multiple range estimates to known positions, its position can be calculated via multilateration. Actual location obtained by APS are on average less than one radio hop from the true position.

• [DPG01] L.Doherty, KS.J. Pister, and L.E. Ghaohi, ''Convex position estimation in wireless sensor network,'' in Proc. IEEE INFOCOM, 2001, vol.3, pp. 1655-1663.

In this paper, a methodology for formulating a sensor network position estimation problem as a linear or semidefinte programe is proposed. It formulates the localization as a convex optimization problem. Convex constraints are presented that can be used to require a sensor's location estimation to be within a radius or angle range from a second sensor. Providing that the constraints are tight enough, simulation illustrate that this estimate becomes close to true position.

• [SRZ+03] Y.Shang, W.Ruml, Y.Zhang and M.P.J. Fromhert, ''Localization from mere connectivity'' in Proc. Mobihoc'03, June 2003,pp 201-212.

This paper introduces a method which only makes use of connectivity to provide position in a network with or without landmark. It is based on classic multidimentional scaling (MDS). The proposed method operates in three stage. The first stage computes the shortest paths between all pairs of nodes in the network. The second stage is to apply classical MDS on this matrix, and retain the two largest eigenvalues and eigenvectors in order to construct a 2D map. The third stage is the conversion to an absolute map if three or more landmark are available. Extensive simulations using various network arrangements and different levels of ranging error show that the method is effective, and particularly so for situations with few anchor nodes and relatively uniform Node distribution.

• [L04] Erik G. Larsssom ''Cramer-Rao bound analysis of distributed positioning in sensor networks,'' IEEE Signal Processing letter, vol.11, no3, pp.334-337, Mar.2004

In this paper, Cramer-Rao bound is derived and simulated for range-based cooperative positioning. All unknown nodes are assumed to determine their position by first performing range measurements on each other, and then solving the corresponding geolocation problem. Numerical results show that the Cramer-Rao bound of the conventional positioning is much worse than the Cramer-Rao bound of cooperative positioning. And clock bias will affect the Cramer-Rao bound.


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[MRW+07] C.L.F. Mayorga, F.D.Rosa, S.A. Wardana, G.Simone, M.C.N.Raynal, J. Figueiras, S. Frattasi '' Cooperative positioning techniques for mobile location in 4G Cellular network'' IEEE International Conference on Pervasive Services, pp:39 – 44, Jul. 2007

The major contribution of this paper is the development of a cooperative mobile positioning system supported by a hybrid WiMAX/Wi-Fi network, BS-MS link adopted the IEEE802.16e standard and Cross-correlation based TDOA estimation is employed. MS-MS link adopted the IEEE802.11a standard using RRS measurement to estimate the distance between two cooperative MS. The available TDOA measurements are used to estimate the positions of the MSs by using the least square (LS) algorithm. These estimates are then applied as initial guesses to the non-linear least square (NLLS) algorithm, which only in the cooperation case uses both the available TDOA and RSS measurements in input. Simulations show that cooperation brings down the average location error far below the FCC requirements, much more than the case without cooperation.

[WZ08] Zhonghai Wang; Zekavat, S.A., "NET 14-2 - A Novel Semi-Distributed Cooperative Localization Technique for MANET: Achieving High Performance," Wireless Communications and Networking Conference, 2008. WCNC 2008. IEEE , vol., no., pp.2414-2419, March 31 2008-April 3 2008

This paper introduces a semi-distributed cooperative localization technique realized via multi-node time-of-arrival (TOA) and direction-of-arrival (DOA) optimal fusion: Each base-node estimates the position of target-nodes by joint TOA- DOA evaluation, and then, the target-node position estimation error is minimized by TOA-DOA optimal fusion across multiple base-nodes. The performance of the proposed technique is studied and compared to two GPS-based positioning techniques, i.e., GPS-aided TOA fusion and GPS-aided DOA fusion. The circular error probability (CEP) is derived theoretically and verified via simulations. The results confirm the superiority of the proposed localization technique in moderate scale mobile ad- hoc networks (MANETs) compared to the two GPS-based fusion schemes. Thus, while the proposed technique is applicable to MANETs in GPS-denied environments, it is also suitable for GPS available environments. Finally, compared to the centralized scheme, the positioning updating rate of the semi-distributed technique is higher and its power consumption in the reference base node is considerably lower. Specifically in relation to cooperation, the paper considers formation of clusters with the objective of maximizing the positioning update rate. The authors argue that to increase this rate, the data processing time must be lowered by distributing data processing across multiple base nodes.

[APA+06] Nayef A. Alsindi; Kaveh Pahlavan; Bardia Alavi; Xinrong Li, "A Novel Cooperative Localization Algorithm for Indoor Sensor Networks," Personal, Indoor and Mobile Radio Communications, 2006 IEEE 17th International Symposium on , vol., no., pp.1-6, Sept. 2006

This paper introduces a distributed localization algorithm, ”Cooperative Localization” with Optimum Quality of Estimate, which uses recently developed channel models to enhance the accuracy of indoor positioning. These channel models are based on outdoor-indoor and indoor-indoor measurements of 3GHz bandwidth UWB, and they characterize the path-loss and the distance measurement error (DME). The algorithm includes steps for selection of anchor nodes, which are used by non-anchor nodes to perform localization using an LS approach. Nodes select which anchor nodes to use for localization based on the quality of link (QoL), which is an aggregate of the variances in the DME model of the node-anchor link. Further, the authors introduce a quality of estimate (QoE) metric to reduce error propagation in the network. The proposed algorithm is compared to a non-channel localization algorithm that selects anchors based on TOA or distance in a simulation study. In this comparison, the proposed algorithm is shown to give substantial accuracy improvements in the considered indoor scenario.

[RDH07] Rengasamy, M.; Dutkiewicz, E.; Hedley, M., "MAC design and analysis for wireless sensor networks with co-operative localisation," Communications and Information Technologies, 2007. ISCIT '07. International Symposium on , vol., no., pp.942-947, 17-19 Oct. 2007

This paper describes the design and development of a contention-free MAC protocol for supporting cooperative localization. The authors discuss the properties of existing MAC protocols such as IEEE 802.11, Z-MAC, 802.15.4 MAC, and WiMedia MAC. They adapt the WiMedia MAC to fit the


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requirements of cooperative localization in WSNs and they evaluate, by simulation, the time it takes for the network to become contention-free upon network start-up and when new nodes join the network.

[FWW08] U.Ferner, H.Wymeersch, M.Z.Win “Cooperative Anchor-Less Localization for Large Dynamc Networks”, Proc. of the 2008 IEEE International Conference obn UWB (ICUWB2008)

This paper presents a cooperative positioning algorithm for use when there is no fixed localization infrastructure available. This algorithm is based on the theory of factor graphs and the sum-product algorithm. Agents are deployed in an environment, know their initial position, and cooperate to localize while they are moving. This paper shows that cooperation can be achieved in a distributed and scalable manner. Performances are evaluated for large scale UWB networks. Results are based on an extensive measurement campaign and indicate that the present algorithm outperforms conventional cooperative techniques in terms of accuracy and robustness.

[CDW08] A.Conti, D.Dardari, M.Z.Win “Experimental Results on Cooperative UWB Based Positioning Systems” ”, Proc. of the 2008 IEEE International Conference obn UWB (ICUWB2008)

This paper presents the first experimental results of cooperative positioning using UWB nodes. A realistic indoor scenario is considered where a number of beacons (anchor nodes),are deployed to localize the target(s) using UWB technology. The proposed algorithm is based on multilateration. The original nonlinear least square (LS) problem is classically transformed into a simpler linear LS problem A two-step algorithm is proposed, based on the LS technique, that improves the localization accuracy when topology information of the environment is available. An iterative version of the LS technique is then introduced, that accounts for ranging information from cooperation among targets. Results show that cooperation is not always advantageous, according to the geometric configuration of the devices.

2.4 Others

[LCC+05] A. LaMarca, Y. Chawathe, S. Consolvo, J. Hightower,I. Smith, J. Scott, T. Sohn, J. Howard, J. Hughes, F. Potter, J. Tabert, P. Powledge, G. Borriello, and B. Schilit. “Place lab: Device positioning using radio beacons in the wild”, in Proceedings of the Third International Conference on Pervasive Computing, Lecture Notes in Computer Science. Springer-Verlag, May 2005

PlaceLab is a project developed by Intel Research in Seattle and US universities. Place Lab uses Wifi, or GSM, or even Blue Tooth beacons to estimate user location. In contrast to fingerprinting, PlaceLab relies on the knowledge of beacon locations. This information may be obtained from institutions, or alternately from data base collected by “war-drivers”. War-driving is the act of driving around with a mobile computer equipped with a GPS device and a radio (typically an 802.11 card but sometimes a GSM phone or Bluetooth device) in order to collect a trace of network availability. wigle.net is the largest of the 802.11 war-driving repositories, and contains over 14 million known AP positions (in 2008). The location method itself is not clearly described. It is only said that “The position estimate was computed using a Bayesian particle filter tracker with a sensor model that exploits the fact that observed signal strength and beacon-frame loss rate correlate with distance”. It seems that a propagation model is used to evaluate the user’s distance to each of the base station in its hearing range, followed by some triangulation algorithm. Accuracy results show that with sufficient density, 802.11 beacons alone can provide median accuracy of around 15-20 meters while GSM beacons alone provide accuracy of 100-200 meters. The Place Lab software is released under an open source license at http://www.placelab.org/

3. Localisation for Communications Use of location for communication is presented. Here, location means not only geographical coordinates, but also speed and direction. Location information may help hand over decisions. Anticipating mobile location can avoid handover towards a very small cell, while at high speed. It can also mitigate the so-called “ping-pong effect”, where shadowing is mistaken for mobile being at cell edge. Lastly, in a relay-enabled network, relay choice may benefit from location information.


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3.1 Location based RRM

• [HC+02] M. Hildebrand, G. Cristache, K. David, and F. Fechter, “Location-Based Radio Resource Management in Multi Standard Wireless Network Environments”, Proceedings of the IST Mobile & Wireless Communications Summit, June 2002.

This article discusses new RRM schemes where one of them is location based. Therefore, two concepts for targeting RRM challenge are elaborated:

• Multi-standard radio resource management (MxRRM) and • Location based radio resource management.

By MxRRM the authors understand traffic and radio resource management in scenarios where more than one wireless network provides coverage, like GSM, GSM/GPRS, UMTS, Bluetooth, and/or W-LAN. If a mobile supports several radio access technologies the question arises which one should be used for a particular communication? To answer this question, first it has to be investigated which systems are available. Secondly the decision has to be made which one of these to use, based on service, capacity and current load. For optimized decisions in such an environment, meaning maximum capacity, best QoS (Quality of Service), and minimum energy consumption of the mobile station, utilizing the position of the mobile station is discussed and shows promising results.

3.2 Location based handover:

• [MT+07] C. Mensing, E. Tragos, J. Luo, and E. Mino, “Location Determination Using In-Band Signaling For Mobility Management in Future Networks”, Proceedings of the 18th IEEE International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), Athens, Greece, September 2007.

A section from [MT+07] is referring to the WINNER project [WIN08]: Location information can also be used to improve system operations of networks by including the spatial distribution of users and assets for communications (e.g., routing) and intersystem handover [WIN08]. An indispensable precondition to achieve integration of different networks is the possibility to allow for execution of handover between these systems. In a homogeneous system the scanning of other possible connections is triggered by the condition of the link, since an ongoing connection with good performance makes such a procedure dispensable. For an intersystem handover (ISHO), continuous surveillance is mandatory. Therefore the MS must scan all possible radio access technologies. The autonomous gathering of information by means of scanning may impact both the own and other transmissions. In all cases, the location-based Inter-System Hand-Over (ISHO) in combination with the so-called hybrid information system (HIS) [WIN08] offers a great economic potential since participating devices can minimize or even avoid self-driven scanning procedures. The principle of the HIS presumes that each system collects data about the current link state within the covered cell and provides this information on request to MSs that are willing to change their connection within the same system or different systems. The basic idea behind the HIS approach is that each system reports about the current state, i.e., the link condition including, e.g., interference distribution. Together with a measurement report the location of the reporting MS within the covered cell is registered. The data is stored in a data base (DB) such that MSs of another system willing to change may request this information. Depending on the new target system and the current location of the MS, the MS is supplied with state reports of the same system type or another system, and subsequently may perform the handover, which is referred to as location-based ISHO since the location of the MS is exploited in the handover process. Obviously, the less accurate and precise the location information, the larger the difference between the anticipated, i.e., retrieved measurement report, and the real link condition in the target system after the handover. In the following the proposed position-based, location-aided handover is explained in more details (Figure 5). Each active MS reports about the current link condition (1); together with the measurement report, the location of the reporting MS is stored in a DB. A MS that intends to perform a handover sends a request to its BS, see (3). The BS in turn acquires the corresponding measurement report from the DB, depending on the current location of the MS (4), and signals the handover decision (respectively related information that allow the MS to take the decision) to the MS (5). The MS can then perform the handover, which is marked by step (6) in Figure 5.


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Figure 5: Location based ISHO using the HIS

• [KFP+04] K. Kastell, A. Fernandez-Pello, D. Perez, R. Jakoby, R. Meyer, “Performance advantage and use of a location based handover algorithm,” in Proc. Vehicular Technology Conf., 2004, Vol. 7, pp. 5260–5264, Sept 2004

In this paper, the handover performance within UMTS and GSM, and between the two, is assessed. In order to reduce the handover preparation time, the built-in localization mechanisms of UMTS and GSM are used instead of measuring the reception level of surrounding cells. A swift handover is desirable in order to enhance calls quality, particularly in scenarios where the user stays in the cell only for a short period of time, namely because of its high speed. The suitability of the built-in localization mechanisms in UMTS and GSM, in order to determine the next cell during handovers, is discussed and possible new methods and enhancements to help further reduce localization measurements are suggested.

• [LCC+06] F. Lassabe, P. Canalda, P. Chatonnay, F. Spies, D. Charlet, “Positioning Awareness: an Essential Component for Mobile Multimedia Applications,” in Proc. International Conf. on Distributed Frameworks for Multimedia Applications, 2006, pp. 1–8, May 2006

The requirements and solutions to address WiFi service continuity in mobility are the main subject in [L+06]. A solution for this involves terminal positioning, which is achieved through trilateration by computing distances according to signal strength. The handover, which is done transparently by an adapted protocol, requires an anticipation which is achieved through mobility prediction. Several methods of determining the mobile terminal position are enumerated, and a trilateration method based on the RADAR project approach [BP00] is described in detail. A WiFi mobility management middleware is presented, consisting in two main parts: the positioning of the mobile terminal and the mobility prediction. Vertical handover is managed through a protocol still in development, which should be used to communicate in unconnected mode.

• [JLL+06] Rong-Terng Juang, Hsin-Piao Lin, Ding-Bing Lin, and Wei-Cheng Zeng “Verification of Mobility-based GSM/WCDMA Intersystem Handover Using Measurement Data”. (PIMRC'06)

The authors consider intersystem handovers between UMTS and GSM networks. Handovers are generally triggered by the RSS falling below a certain threshold. However, because of shadowing, the RSS from the nearest base station may be weaker than that from distant base stations. Then, shadowing being a spatially random process, handover decision mechanisms based on measurements of signal strength induce the so-called “ping-pong effect “. Indeed, the mobile user may well leave the shadowing area as soon as handover is performed, giving rise to a reversed handover back to the initial cell. This ping pong effect causes unnecessary signalling, and capacity loss. The


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authors propose to add a location criterion to the simple threshold criterion. Assuming that the mobile user is GPS enabled, they propose to. use a more stringent criterion for handover decisions by introducing a parameter of link robustness, determined from the distance between the mobile and surrounding base stations, thus adjusting the threshold to suppress unnecessary handovers. Ping pong effect is then shown to be mitigated.

• [LJL04] H.-P. Lin, R.-T. Juang, D.-B. Lin, “Improved location-based handover algorithm for mobile cellular systems with verification of GSM measurements data,” in Proc. Vehicular Technology Conf., 2004, Vol. 7, pp. 5170–5174, Sept. 2004.

• [JLL05] R.-T. Juang, H.-P. Lin, D.-B. Lin, “An improved location-based handover algorithm for

GSM systems,” in Proc. IEEE Wireless Communications and Networking Conf., 2005, Vol. 3, pp. 1371–1376, Mar. 2005.

• [LJL05] H.-P. Lin, R.-T. Juang, D.-B. Lin, “Validation of an improved location-based handover algorithm using GSM measurement data,” in IEEE Trans. Mobile Computing, Vol. 4, No. 5, pp. 530–536, Sept.-Oct. 2005.

The handover algorithm proposed in [LJL04], [[JLL05]], [LJL05] identifies the correlation among shadowing components based on the estimates of mobile terminal velocity, to suppress the handover “ping-pong effect” induced by handover decision mechanisms based on measurements of signal strength, whose variation caused by shadowing is a random process. Frequent handovers reduce the QoS, increase the signaling overhead on the network, and degrade throughput in data communications. The proposed location based handover algorithm is claimed to overcome the lack of feasibility of other enumerated handover algorithms based on mobile terminal location info, mainly because of their excessive computational complexity. The algorithm is based on the estimates of mobile terminal location and velocity, without GPS aid, in a lognormal fading environment. It makes handover decisions for cellular systems, based on mobile terminal location and velocity, and exploits the correlation properties of shadow fadings to avoid unnecessary handovers in the overall environment, while having low computational complexity and not requiring a database or lookup table.

• [P-COM96] G. P. Pollini, “Trends in handover design,” IEEE Communications Magazine, pp. 82–90, Mar. 1996.

An essential component of mobility management in cellular networks is the handoff algorithm design. It is another important component of mobility management in cellular networks. Handoff is the process whereby a mobile communicating with one set of base stations is switched to another set of base stations during a call. A survey of handoff initiation algorithms is given in [P-COM96]. Conventional approaches compare the absolute and/or relative signal strength measurements with some predetermined threshold, which are ad hoc in nature and bear no optimality [P-COM96], [ZH-VTC94]. A new class of handoff algorithms is developed recently in [KES-ICC94], [PV-JSAC00],[PV-VTC00], [VK-TVT97], based on various optimization criteria that require knowledge of the mobile terminal velocity and position. Although only local optimality can be achieved, these methods are adaptive in nature, which is the principal advantage over the conventional handoff schemes.

• [CMD+06] E.Cianca, A. Molinaro, M. De Sanctis, A. Iera, G. Araniti, M. Ruggieri “Location/situation-aware architecture for mobility management over heterogeneous networks “Proceedings of the 2nd international conference on Mobile multimedia communications, Alghero, Italy, 2006

• [CDA+08] E. Cianca, M. De Sanctis, G. Araniti, A. Molinaro, A. Iera, M.Torrisi and

M. Ruggieri “Integration of Navigation and Communication for Location and Context Aware RRM” Satellite Communications and Navigation Systems, ISBN 978-0-387-47522-6 Springer 2008


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In these two papers, the authors consider the issue of vertical handovers between systems. They show how user location knowledge can help the handover process. An agent-based middleware uses location information, to inform the user of the list of candidate networks. Indeed, in a multi-networks environment, the terminal will classically scan its environment. However, this process is energy consuming. Here, instead, this information will be communicated to him, based on his location, obtained for instance by GPS. A factor for selecting a network or not is the prediction of the average time in which the user will stay in the coverage area of each access network. Indeed, it is counter productive to perform a handover for a very short time: in this case, the capacity lost during two handover procedures in a row is not compensated by the improved quality in the new network. This prediction can be performed through the knowledge of the terminal location, of its speed and direction and of the maps of coverage areas. If the estimated time is smaller than the time needed to finish the handover procedure, that access network is not even considered in the list of available access networks. The “wake-up” time of the concerned air interface has also possibly to be considered as well as the time between location updates.

3.3 Relay based cooperative communications

• [ZR03] M. Zorzi and R.R. Rao, “Geographic random forwarding (GeRaF) for ad hoc and sensor networks: multihop performance,” IEEE Trans. Mobile Computing, vol. 2, no. 4, pp. 337-348, Dec. 2003.

In this paper, a forwarding technique, called Geographic random forwarding (GeRaF), based on geographical locations of the nodes is presented. This protocol is a best-effort one, where the relay node is not known a priori by the sender, but decided after the transmission has taken place. It is assumed that each node knows (perfectly) its position and the position of the destination node, and that a channel contention scheme exists to determine the forwarding node. Furthermore, the paper presents more routing protocols based on geographic information include GPSR, GEAR, LAR and DREAM.

• [BK+06] A. Bletsas, A. Khisti, D.P Reed and A. Lippman, “A simple Cooperative diversity method based on network path selection,” IEEE Journal on Selected Areas in Communications, vol. 24, no.3, pp. 659-672, Mar. 2006.

In this paper a scheme is presented that each relay, assuming knowledge of its own location information, could assess its proximity toward source and destination and based on that proximity; contend for the channel with the rest of the relays. Note however that while solving the problem of relay scheduling, geographic random forwarding (GeRaF) does not achieve the diversity advantage of the other cooperative networks. This is due to the fact that each potential relay only receives a single version of the message, either from the source or from the current relay.

• [ZV05] Bin Zhao and M.C. Valenti, “Practical relay networks: a generalization of hybrid-ARQ,” in IEEE Journal on Selected Areas in Communications, vol. 23, no. 1, pp. 7-18, Jan. 2005.

In this paper a method to introduce a diversity effect to geographic random forwarding (GeRaF) is explained; it relies on the nodes keeping previously received information concerning each active message until it is able to decode the message correctly. Then it can act as a node to relay and forward the message.

4. Conclusions In the WHERE project communication techniques in heterogeneous networks are an enabler for accurate positioning information. In this deliverable a survey about different positioning techniques are presented. The three key techniques are fingerprinting, time and angle measurements used for geometrical calculations, and cooperative positioning. In fingerprinting we distinct between offline and online phase. The offline phase measures values, like RSS. The data is then compared by an online-learning algorithm to use real-time data based on either deterministic of probabilistic algorithms. All the fingerprinting methods use different communication techniques that have different bandwidths, different coverage and all this leads to different accuracies to position the mobile terminal. The survey gives an overview about potential accuracies. In the WHERE project a fingerprinting data base is generated in WP4 and will be used in WP2 and WP4. The survey gives an overview of potential techniques that will be complemented in the ongoing research.


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Geometrical techniques are based on measurements of the used signal either by time-of-arrival or angle-of-arrival measurements. The techniques are introduced and several references are outlined that combine a set of measurement techniques. All of these techniques will be covered and addressed in the WP2 which focuses on hybrid data fusion. Cooperative positioning is derived from sensor networks and robotic communications. The challenge in heterogeneous networks of today is the ever changing environment, the unknown number of active participants and the unknown infrastructure. The information collection is not centralized. The exchange of information by cooperating mobile terminals helps to improve the accuracy of the position of all involved. Cooperative positioning is part of WP2 in the WHERE project and the state-of-the-art research references will be investigated deeper in the remaining period. Positioning information is only of partial help to improve communications. Additional information can be derived from predictions (navigation information) about the future based on the mobility of the mobile user and its surrounding users. Handover within a homogenous system are steered by the network operator. In a heterogeneous network the driving force could change from the network operator to the user. Networks which are not aware of each other could be able to cooperate by the localization based knowledge of the user. This could be used in relay based networks, where the relay could be either managed by the operator or it could be another mobile terminal. Both are of interest for the WHERE project to enhance the coverage and the overall throughput in communication networks.

5. References [ACY04] S. Al-Venkatraman, J. Caffery, H-R. You, “A novel TOA location algorithm using LOS range estimation for NLOS environments”, IEEE Transactions on Vehicular Technology, Vol. 53, pp.1515- 1524, September 2004 [ACY07] S. Al-Jazzar, J. Caffery, H-R. You, “Scattering-model-based methods for TOA location in NLOS environments”, IEEE Transactions on Vehicular Technology, Vol. 56, pp.586- 593, March 2007 [APA+06] Nayef A. Alsindi; Kaveh Pahlavan; Bardia Alavi; Xinrong Li, "A Novel Cooperative Localization Algorithm for Indoor Sensor Networks," Personal, Indoor and Mobile Radio Communications, 2006 IEEE 17th International Symposium on , vol., no., pp.1-6, Sept. 2006. [ASW06] F.Althaus, C.Steiner, A.Wittneben, “UWB Geo-Regioning Algorithm and Performance” Workshop on Positioning, Navigation, and Communication, WPNC’06 [BP00] P. Bahl and V. N. Padmanabhan, “RADAR: An in-building RF-based user location and tracking system,” in Proc. IEEE INFOCOM, 2000, Vol. 2, pp. 775-784, Mar. 2000. [BK+06] A. Bletsas, A. Khisti, D.P Reed and A. Lippman, “A simple Cooperative diversity method based on network path selection,” IEEE Journal on Selected Areas in Communications, vol. 24, no.3, pp. 659-672, Mar. 2006. [BL06] J. Borkowski and J. Lempiainen, “Practical Network-Based Techniques for Mobile Positioning in UMTS”, EURASIP Journal on Applied Signal Processing, Volume 2006, DOI 10.1155/ASP/2006/12930. [BL08] A. Baggio and K. Langendoen, “Monte-Carlo Localization for Mobile Wireless Sensor Networks”, Ad Hoc Networks, Delft University of Technology – The Netherlands, July 2008. [BP00a] P. Bahl and V. N. Padmanabhan. RADAR: An in-building RF-based user location and tracking system. In INFOCOM, pages 775–784, 2000

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[LCC+05] A. LaMarca, Y. Chawathe, S. Consolvo, J. Hightower,I. Smith, J. Scott, T. Sohn, J. Howard, J. Hughes, F. Potter, J. Tabert, P. Powledge, G. Borriello, and B. Schilit. “Place lab: Device positioning using radio beacons in the wild”, in Proceedings of the Third International Conference on Pervasive Computing, Lecture Notes in Computer Science. Springer-Verlag, May 2005

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ICT 217033 WHERE D6.2 Version 1.0 Survey on localisation in communication networks