An RFID Based Location Finding and Tracking With

An RFID Based Location Finding and Tracking with

Guidance

Rusen Oktem, Elif Uray Aydin

Electrical and Electronic Engineering Department

Atilim University

Ankara, Turkey

E-mail: {rusen, eaydin}@atilim.edu.tr

Nergiz Ercil Cagiltay

Software Engineering Department

Atilim University

Ankara, Turkey

E-mail: [email protected]

Abstract—This paper tackles an RFID based location finding and

tracking system. The system is an integral part of a navigation

aid being developed for guiding visually disabled people in a

store. The aid is composed of a portable hardware interface unit,

a standalone RFID unit, and a central processing unit. The units

interact via wireless communication to locate the position of the

user in a known indoor environment and tracking his/her

movement. An active RFID tag helps to estimate the location of a

user and the user is guided to follow a route accordingly, via a

tactile compass. The system uses RF signal strengths and is based

on Bayes Decision Theory. Initial simulation results with the

system prove promising for location finding and tracking, especially when the tracked person is guided by a system.

Keywords-location estimation, RF signals, Bayes, RFID, classification, tracking.

I. INTRODUCTION

Navigation aids generally comprise either an outdoor or indoor positioning system or both, for detecting the position of the user [1-8]. The technology used in location estimation depends on the type of the subject environment (indoor, outdoor, large scale, etc.) and also on the application. For example, GPS or GIS based systems are often utilized for large scale outdoor environments [1,2,3]. On the other hand, a pair of AM radio signal transmitter and a receiver, producing beep sounds when triggered, is preferred for the sake of simplicity and low cost, when a person wants to know the location of a particular object in a small scale outdoor environment [4]. For indoor environments, multiple ultrasound or infrared transmitters mounted on the walls or at the ceiling can be used [2,5,6,9]. Such a system enables estimation of the location of the receiver, by using received signal strength. However, line-of-sight requirement of ultrasound and infrared sensors limit their use in obstructed environments. Use of passive RFID tags in a grid like structure is another alternative to estimate the location with respect to the nearest transmitter [7]. The radio signals do not require line-of-sight, and radio signal transmitters are easy to integrate into wireless networks. Hence, they have been particularly used for network based location estimation purposes [10-12]. Radio signal based location estimation algorithms exploit either received signal strength (RSS), time of arrival (TOA), or related observations. However, indoor environments possess various structures which result in reflection, refraction, and diffraction of radio waves. Hence, such observations often deviate from assumed

mathematical models at indoor environments. Location estimation algorithms have to offer either statistical methods or have to rely on vast amount of data in order to obtain a sufficiently accurate positioning.

In the proposed system, a medium scale obscured indoor environment is focused on, and an RFID based positioning method is developed. RSS levels from three UHF transmitters are processed for estimating the location of the RFID tag. The system is an intagrated navigation and information access system which enables not only navigation of visually impaired people through aisles of a shopping store but also access the information about products. Instead of communicating through sound, a special compass is used to direct the user. The system also does not require the user to carry a computer system. In this system, the communication between the system and the user through sound system is very limited to minimize the interference with the visually impaired shopper’s perception of environmental sound. The navigation system incorporates a novel compass design, RFID tags, and wireless network to direct the user to the desired location in a building. The information access system includes a camera to read barcodes of a product and wireless communication to inquire information about that product through the bar code from the centre’s database. The system differs from existing projects in that field in terms of usability and cost. Its main aim is to help the visually impaired people do shopping without getting help from the environment.

II. MAIN COMPONENTS OF THE NAVIGATION SYSTEM

The navigation system consists of three main units: a

hardware interface unit (HWIU), a data collection and wireless

communication unit (DCWCU), and a central processing unit

(CPU). Fig. 1 summarizes the general structure of this system.

Figure 1. General Structure of the System

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Authorized licensed use limited to: S R R Engineering College. Downloaded on October 3, 2009 at 02:22 from IEEE Xplore. Restrictions apply.

The hardware interface unit (HWIU) provides user

interaction via a touchpad, a generic tactile compass and a

speaker. HWIU is the only device that will be carried by the

user, and it will be presented to him/her at a preset location

inside the store. User will receive verbal instructions from

HWIU about how to use the device, and will provide the

information about which aisle to go via its touchpad. DCWCU

receives this information from HWIU and transmits it to CPU.

CPU computes a path for the user to guide him/her from the

initial location to the desired one. An RFID based data

collection system which is a part of DCWCU collects data to

tag the position of the HWIU. CPU processes that data to

estimate the position of the user. By checking the pre-stored

path, CPU decides which orientation to direct the user to. This

orientation information is sent to HWIU by DCWCU. Upon

receival of this information, the tactile compass points to the

desired orientation to aid the user to follow the pre-stored path.

The data collection unit is an RFID based one working at

433MHz range. Its operation is rather standalone, and is

composed of three (or more) transmitters, an RFID tag, and a

receiver. The transmitters broadcast at distinct frequencies.

They are mounted on the ceiling of the market area, in a way to

form a triangle as in Fig. 1. The tag is mounted on the HWIU,

hence is carried by the user. The tag receives signal strengths

from the three transmitters, tags them according to the signal’s

frequencies, and transmits to the receiver in a predefined order.

The tagged signal strengths are mapped to a signal strength

identification value (RSSI) which is proportional to the RSS,

and collected at the CPU via serial connection between the PC

and the receiver.

CPU is composed of a PC, and software running on it. The

software has three main algorithms for:

• path finding

• positioning

• barcode recognition [13]

The developed GUI which also controls the sequential

operations of the three algorithms is presented in Fig. 2.

Fig. 2. The Graphical User Interface

A. Path Finding Algorithm

The path finding algorithm is developed to establish the

following jobs:

- Getting location information from the positioning algorithm

and the selected destination address from the user,

- Calculating the shortest path between the starting and

destination points,

- Extracting the next direction (direction from the current cell

to the next cell) information and transmitting it to the HWIU.

In our system, A* algorithm uses a starting point and a

destination point to produce the desired path, if it exists.

Each time the user request direction information the CPU

gets data from the data collection unit and estimates the current

location of the user. Then it sends the direction information to

the compass unit, according to the previously defined path

information. This process continues recursively until the

destination is reached.

The CPU also has an administer interface to setup the

shopping store structure (shopping units, obstacles, walking

paths, start points and exit points) in the computer system

which is developed in Java, Netbeans environment. Through

the interface, the shopping store’s map can be entered to the

system by a graphical display as shown in Fig. 2.

B. Positioning Algorithm

RSSI values collected at the CPU are measure of the

power received by the RFID tag from a transmitter and provide

information as to location of the subject carrying it. The

received signal consists of direct, reflected, scattered and

diffracted waves. For indoor environments, RSSI values are

assumed to follow the convenient empirical model expression

[14].

)log(dBARSSI += (1)

where d represents the distance between the transmitter and

the tag, and A, B are parameters to be estimated. At ideal

conditions without the existence of any reflections, diffractions,

and scattering, the distance of the tag to the three transmitters,

hence the location of the tag, can be computed from Equation

(1), when A, B are known. However, in our application, this

empirical formula does not lead to reliable positioning and the

application calls for incorporation of statistical methods.

III. LOCATION ESTIMATION TRACKING WITH BAYESIAN

APPROACH

Consider an experimental study where the outcome

(observation) is a feature vector x corresponding to a pattern.

Assume that there exist N classes NCCC ,,, 21 where the

observed pattern belongs to either one of these classes. The

problem is stated as finding the class Ci, to which the observed

pattern is most likely to belong [15]. For this purpose, often a

discriminant function g(x) is used as:

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classify x in Ci if gi(x) > gj(x)ij ≠∀

.

The discriminant function gi(x) is often defined as;

( ) ( ) ( ))(ln)(ln)()(ln)( iiiii CPCpCPCpg +=⋅= xxx (2)

where )( iCp x and

)( iCP refer to conditional probability

density function and probability of the ith class, respectively.

Fig. 3. Part of the subject environment. ‘s represent

transmitters.

Let us assume that the received signal strength

identification levels from the three transmitters by the RFID

tag constitute feature vectors x. The indoor environment

shown in Fig. 3 is divided into 120cmx120cm (2 x2 ) square

grids. We assumed each grid as a class. For each class, we

recorded 52 measurements from each transmitter, at the

corners and at the center of the class, and when the person

carrying the tag, and hence the tag antenna is facing different

orientations. We investigated the histograms of these

recordings for each class and decided to use Gaussian

distribution assumption.

Note that the signal strength transmitted by each

transmitter is independent from each other. Hence, the

conditional probability distribution model for the feature

vector x given class Ci can be expressed as;

∏=

−−=3

1

2

22/1)(

2

1exp

)2(

1)(

l

lil

lili

i xCp µσσπ

x

(3)

The statistics 2

liσ and liµ

conditional variances and

conditional mean vector are evaluated by computing sample

variances and sample means for each class and each

transmitter, by use of the recorded measurements.

We assume that the person carrying the RFID tag starts

navigating in the environment from a known location at

time 0=t . This is the location where the navigation aid will be

presented to the user and will be activated. The observations

(feature vectors) are received at constant time intervals t∆

(around 1 sec). At every t∆ interval, the previous location of

the person is assumed to be known. Furthermore, the central

system processing the RSSI levels wirelessly sends a direction

(out of 8, north, south, east, west and the ones in between) to

the hardware unit carried by the user. The direction is signaled

to the user via a tactile compass, of which knob is sensed by

the thumb of the user. Hence, we also assume that the user is

most likely to follow this signaled direction than moving to an

unpredictable direction. Based on this assumption, (2) is

updated as;

( ) ( ))(ln)(ln)( ikii CPCpg += xx (4)

Substituting (4) into (5), we obtain the decision function as

( ))(ln)(2

1exp

)2(

1ln)(

3

1

2

22/1 ik

l

lil

lili

i CPxg +−−==

µσσπ

x

(5)

where )( ikCP refers to the transition probability, that is,

probability that the user used to be in class Ck in the previous

time of reading and displaced to class Ci, at the current time of

reading. xl is the observed RSSI at the current location for the

lth transmitter. Then, given the previous location of the RFID

tag, the current location of it is estimated as the Ci that

achieves

)()( xx ji gg ≥, jiAji ≠∈∀ ,, (6)

where A refers to the set of available classes.

IV. EXPERIMENTAL RESULTS

The results of this proposed system tested through

experiments with positioning algorithm. The proposed

positioning algorithm is tested in the indoor environment

presented by Fig. 3. In tests, the following apriori information

are utilized:

• The statistics (conditional mean and conditional

variance) of the feature vectors are available.

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• The subject’s displacement at t∆ time interval is

limited.

• Three readings from each transmitter can be recorded

by the CPU at each t∆ time interval

• The locations of NON ACCESSIBLE classes are

known by the tracking system.

Table I presents the test results with certain routes. It is

further assumed that the subject carrying the RFID tag is

navigated to follow a certain route (which is provided by the

path finding algorithm), hence a bias favoring a certain class

exists. Then, the transition probabilities between classes are

arranged such that the probability of staying at the same class

is smaller than the average, the probability of transition from

the previous class to the directed class is higher than the

average, and the rest are inversely proportional with the

distance to the previous class. The bold results in Table I show

the false estimations. The results show that the user can be

tracked at a very high performance. Among the six presented

cases, no estimation error occurred at three (second, fourth,

and sixth rows). For most of the false estimations, either one

of the connected neighbors of the correct class is misdetected.

Occasionally, (between classes C38-C18, C45-C43) false

detection exceeding the connected neighbor occurs. The

highest estimation error reaches 2.7m, at 4% of the trials.

TABLE I Tracking performance with navigation bias

Start-

destination

Correct route Estimated route

C28-C39 C28-C27-C22-C21-

C18-C17-C38-C39

C28-C27-C22-C21-

C18-C16-C18-C39

C1-C16 C1-C2-C7-C8-C9-

C14-C15-C16

C1-C2-C7-C8-C9-

C14-C15-C16

C25-C41 C25-C24-C36-C37-

C38-C39-C40-C41

C25-C23-C35-C36-

C37-C38-C39-C41

C18-C8 C18-C20-C15-C14-

C9-C8

C18-C20-C15-C14-

C9-C8

C28-C45 C28-C25-C24-C36-

C37-C38-C39-C40-

C41-C42-C43-C44-

C45-C45

C28-C25-C23-C35-

C36-C37-C38-C39-

C41-C42-C43-C44-

C43-C44

C35-C1 C35-C24-C23-C22-

C21-C20-C15-C14-

C9-C8-C7-C2-C1

C35-C24-C23-C22-

C21-C20-C15-C14-

C9-C8-C7-C2-C1

V. DISCUSSIONS AND CONCLUSIONS

This paper proposes an active RFID based location

estimation and tracking system which is a part of a navigation

aid to be designed for guiding visually impaired people in a

store. Location finding is based on Bayes decision rule, where

an observed feature vector is classified to a class among the

set of available ones. The classes are modeled as accessible

square grids in an indoor environment. The simulation results

show that the user can be tracked at a high performance

especially when a guidance favoring a certain route exists.

Acknowledgment

This work is supported in the scope of project ID 105E130 by

the Scientific and Technological Research Council of Turkey

(TUBITAK).

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An RFID Based Location Finding and Tracking With