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Location Estimation Media, Algorithms and Systems
Stephane BoreuxAlexander Hörmandinger
Introduction- Explosive trend towards mobile computing- Location is a fundamental factor of the
application / usage context- Location can be:
(i) Physical location(ii) Symbolic location
- Location can be expressed:(i) Absolute location: one reference grid for all located objects(ii) Relative location: objects have own / different reference grids
Agenda• Location estimation media• Proximity• Triangulation• Indoor systems:
- Cricket system- Hitachi Air System
• Outdoor systems:- Galileo GNSS
• Scene Analysis- RADAR system
• Positioning in 2G / 3G networks
Location Estimation media• Radio frequency
– Multipath propagation– Shadowing– Large transmission range
• Infrared– Interference with ambient light– Limited transmission range
• Ultrasound– Medium dependent– Lower speed Higher accuracy– Limited coverage
Proximity
• Symbolic informations• Intelligents Services• Example
– IP subnet detection– Active badge
Triangulation (1)
• Time of Arrival (TOA)– Distances– Errors Corrections
• Geometric middle• Clock correction
– Examples• GPS• Active BAT• Cricket System
Cricket system
• RF and ultrasound• Units can be use as beacon or receiver• Mobile unit perform the algorithm
– Privacy– Independent of users number
• Critiques– Number of units needed– Integration in existing system
• Enhanced cricket compass
Mobile device
ZX
Y
Triangulation (2)
• Time Difference of Arrival (TDOA)– Differences between distances– Hyperbolic– Remove clock offset– Examples
• Hitachi AirLocation• TruePosition
– U-TDOA• 3GPP standard
Hitachi AirLocationTM
• Capabilities– Indoor and outdoor– 1 to 3 meters accuracy– Same range as Wifi (30 to 100 m)
• Wireless LAN based System• 3*TDOA• Main purposes
– Distribution and Warehouses– Company offices – Stations, airports, and underground arcades
Hitachi AirLocationTM
Triangulation (3)
• Angle of Arrival– Measure angle of Arrival– Directional antennas– Strong influence of environment– Mostly used in hybrid system– Examples
• TruePosition
Galileo (1)• GALILEO is an European initiative for a state-of-the-art
global navigation satellite system (GNSS) driven by the European Commission and ESA
• Strategic political decision towards independence of GPS and GLONASS
• Under civilian control• Programme phases:
- Development and In-Orbit Validation (2002-2005)- Deployment (2006-2007) -> first satellite launch in December 2005
- Commercial Operations (from 2008)
Galileo (2)• Technically a refined version of GPS - > uses Time of Arrival
approach
3 equal planes with 9 operational satellites + 1 on active spare in each plane
6 equal planes with 4 satellites in each plane
Orbital planes
56°55°Inclination angle of planes (with reference to equatorial plane)
23 222 km22 200 kmOrbital altitude
30 (27 operational + 3 in reserve)
24#satellites
GALILEOGPSCharacteristic
Galileo (3)
Open Service (OS):- Will be free for anyone to access- 2 signal bands - Accuracy: <4m horiz., <8m vertic. when using both signal bandsCommerical service (CS):- Commercial fee- Encrypted, 3 signal bands- Accuracy: < 1m horiz. and vertic. / <10cm when locally augmentedPublic Related Services (PRS)Savety Of Live Services (SoL)
Precise positioning service (PPS):- Originally only for military use - Accuracy: 22m horizontally, 27.7m vertically, 200 ns temporallyStandard positioning service (SPS):- For civil usage- Originally built-in variable error to degrade the accuracy („selective availability“) Original accuracy: 100m horiz., 156m vert., 340ns temp.- Selective availability was deactivated in 2000 -> same accuracy as PPS now
GALILEO servicesGPS services
Scene AnalysisIdea: Include information / characteristics of the scene to infer
the location of an object
General steps:(i) Profiling:
- Coverage area is divided into regions- Multiple samplings of parameters at each observation point are taken and stored in location database -> „fingerprints“
(ii) Matching:- Compares real-time measurments with entries in the location database and finds the „best“ match(es)
(iii) Estimation:- derive location estimation from the „best“ match(es)
Scene Analysis – RADAR (1)
- RADAR is an RF-based system for locating users inside buildings
- Developed by a Microsoft research group- Median resolution is 2 to 3m
RADAR (2) -Testbed
• 3 WirelessLAN Access points (= basestations)
• Mobile host is a laptop with WLAN network card
• Clocks on mobile host and on the base stations are synchronized
• Mobile host broadcasts UDP packets
• Signal strength (SS) is recorded by the basestations
Picture source: 1) (see References)
Profiling - RADAR (3)- Fingerprints: at least 20 samples for each user orientation at 70 distinct
physical locations on the floor (#fingerprints >= 20 * 4 * 70 = 5600)- Location database includes tuples of the form:
),,,( issdyx }3,2,1{∈i
- For each tuple the mean, the standard deviation and the median of the SS values for each of the base stations is computed
Matching - RADAR (4)Empirical method: Compute the distance (in signal space) between the
observed set of SS measurements and the recorded SSDistance function: p
pii
n
i ip em
wnL
1
1||11⎟⎟⎠
⎞⎜⎜⎝
⎛−= ∑
=with p=2 -> Euclidian distance
Random method: Estimate the users location by picking one of the 70 locations at random
Strongest base station method: Guess the user‘s location to be the same as the location of the base station that recorded the strongest signal
Cumulative distribution func. of the error distance of the methods
Picture source: 1) (see References)
Estimation - RADAR (5)- Question: Multiple or single nearest neighbor(s) ?- Multiple neighbors because:(i) Inherent variability in the measured SS -> No reason
to pick only the closest neighbor and reject others that are almost as close
(ii) Error vector (in physical space) to each neighbor is oriented different
- Result: for small k k-neighbors has some benefit- For instance: k=5: 25th percentile of error distance is 1.5m (22% better than using single neig.)- But for large k accuracy degrades rapidly
Other aspects - RADAR (6)Impact of the number of data points?-> For n=20 the median error distance
is less than 33% worse, for n=40 it is less than 10% worse
Impact of number of samples ?-> only a small number of samples are
needed: 1 sample -> median error distance 30% worse, 2 samp. -> 11% worse, 3 samp. -> 4% worse
Impact of user orientation ?High !
Tracking ?Reduce the problem to a sequence of location determination problems-> median error distance is 19% worse than for a stationary user.
Picture source: 1) (see References)
Propagation model - RADAR (7)- Motivation: reduce dependence on empirical data
- Floor Attenuation Factor propagation model:
⎭⎬⎫
⎩⎨⎧
≥<
−⎟⎟⎠
⎞⎜⎜⎝
⎛−=
CnWCnW
WAFCWAFnW
ddndPdBmdP
**
log10)(])[(0
0
n ... the increase of path loss with distance -> derived empirically)( 0dP ... signal power at some reference distance -> empir. or from specification
d ... transmitter-receiver distance
C ... maximum number of walls up to which the attenuation factor makes a difference
nW ... the number of walls between sender and receiver
WAF ... wall attenuation factor -> derived empirically
- Amazing results !
Picture source: 1) (see References)
Location estimation with 2G / 3G networks (1)
- Driving forces:(i) public: emergency services, intelligence(ii) customers ?
- Same basic techniques as in other positioning systems:(i) Time of arrival(ii) Angle of arrival(iii) Signal strength(iv) proximity (cell identification)
- Positioning system classification:(i) Self-Positioning: location estimation is done by MS(ii) Remote Positioning: BSs measure signals from the MS, these measurements are processed at a central site to estimate the location (network-based)
2G / 3G networks – Ericsson MPS- server-based GMPC (Gate-
way mobile positioning center) for positioning requests (PR) from an application
- server-based SMPC receives PR and returns loc. estimation of MS
- Uses standardized Mobile Location Protocol (MLP) between App. and GMPC
- System according to 3GPP standards
- Positioning methods:(i) CGI-TA and ATI for low accuracy(ii) Assisted GPS for high accuracy
Picture source: 12) (see References)
2G / 3G networks – Nokia mPosition
Position tech. in terminal:
(i) Standalone GPS
(ii) Assisted GPS
Position tech. in network:
(i) Cell identification (CI)
(ii) Enhanced CI (CI + Signal strength or Timing Advance
(iii) Assisted GPSPicture source: 13) (see References)
Conclusions
- Same basic techniques used in all types of systems
- A given infrastructure can be used to gain high location accuracy (sometimes)
- 2G/3G LBS architectures are available- 2G/3G systems are in need of a hybrid approach
(GPS) to achieve high accuracy- Current usage of this systems ?- Where is the killer application ?
References1) RADAR: An In-Building RF-based User Location and Tracking System, Paramvir Bahl,
IEEE Infocom 20002) On Indoor Position Location With Wireless Lan, P. Prasithsangaree, IEEE 20023) http://europa.eu.int/comm/dgs/energy_transport/galileo/4) http://www.esa.int/esaNA/5) www.wikipedia.org6) http://www.trueposition.com7) The Enhanced Cricket Compass with Ad Hoc Network, Kisuk Kweon, Computer
Architecture Laboratory, Department of Computer Science, KAIST8) The Cricket Location-Support System, Nissanka B. Priyantha, Anit Chakraborty, and
Hari Balakrishnan, MIT Laboratory for Computer Science, Cambridge9) Position Information System by Local Mobile Phone Network, Yuji Yamamoto,
Hirokazu Sato, Institute of Technology, Shimizu Corporation, Tokyo10) Overview of Radiolocation in CDMA Cellular Systems, James Caffery, IEEE
Communication Magazine April, 199811) Positioning GSM Telephones, Christopher Drane, IEEE Communications Magazine,
April 199812) http://www.ericsson.com/mobilityworld/13) http://www.nokia.com/link?cid=EDITORIAL_357