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- 1. Scout: Outdoor Localization Using Active RFID Technology Xin Huang, Ramakrishna Janaswamy, Aura Ganz Department of Electrical and Computer Engineering University of Massachusetts Amherst, MA 01003 {xhuang; janaswamy; ganz}@ecs.umass.edu Abstract disaster management, battlefield, education, information spaces, and warehouse management. Our The growing convergence among mobile computing paper focuses on outdoor localization, where we devices and smart sensors boosts the development of propose a system, called Scout, which provides a cost- ubiquitous computing and smart spaces, where effective solution for tracking large number of small localization is an essential part for realizing the big objects. vision. Due to their large size, high cost, and high Nowadays, widely accepted outdoor localization power consumption, the current widely accepted systems are GPS (Global Positioning System) and outdoor localization methods based on GPS and cellular-based. GPS relies on a network of 24 satellites cellular techniques are not suitable for tracking placed in orbits and uses time-of-arrival (TOA) numerous small size and limited power objects. In this information and triangulation to determine the object paper, we present Scout, an easy-setup and cost- locations. Cellular-based localization systems utilize effective outdoor localization system based on off-the- different technologies like angle-of-arrival (AOA), shelf active RFID systems and a probabilistic time-of-arrival (TOA), differential time-of-arrival localization algorithm, which is compatible with the (DTOA), and received signal strength (RSS) future smart spaces and ubiquitous computing systems. measurements. An overview of the network based The proposed algorithm is robust to measurement wireless localization technology can be found in [4]. uncertainty and self-adjustive to varying outdoor However, because of their large sizes, high cost, and environments. Using MATLAB simulations, we high power consumption, GPS and cellular-based investigate the system performance dependence on a localization systems are not suitable for tracking number of system and environmental parameters. The numerous small size and limited power objects. simulation results prove that the proposed system is an Due to advantages such as small size, low power accurate and cost-effective candidate for outdoor and low cost, the Radio Frequency Identification localization. (RFID) sensors are widely used to implement ubiquitous computing and smart spaces. With the 1. Introduction capability of providing RSS information current advanced RFID systems have become a potential candidate for mass localization. Several RFID based Wireless communications, portable computers, and systems have been proposed for tracking objects in smart sensors are continuously becoming popular in indoor environments. SpotON [3] uses an aggregation our social and economic lives and boosting the algorithm for three-dimensional localization. The tags development of ubiquitous computing and smart use RSS information to obtain inter-tag distances based spaces, where physical objects can communicate with on empirical mapping between the two. SpotON users and provide personalized and context-aware assumes deterministic mapping between RSS and services. This in turn fuels the need for developing distances and does not account for the range localization systems capable of tracking objects in both measurement uncertainty caused by the varying outdoor and indoor environments. Such systems can be environments. LANDMARC [5] utilizes RSS used in many scenarios such as road vehicular tracking, measurement information to locate objects using k nearest reference tags. To diminish the uncertainty of Acknowledgement: This project was supported in part by the the detected range caused by the varying environments, following grants: NSF-ANI-0434985, NSF-ANI-0319871, NSF-ANI- there must be a large number of reference tags 0230812, NSF-EIA-0080119, ARO-DAAD19-03-1-0195. 1-4244-0425-8/06/$20.00 ©2006 IEEE
- 2. distributed in the environment. This is impractical for “object” are interchangeable terms in this paper. most of the outdoor scenarios like road vehicular Reference tags are the active RFID tags used for tracking, disaster site, and battlefield. calibrating environmental parameters. Every reference In this paper, we present Scout, an easy to deploy tag also has a unique ID. Once being activated, the and cost-effective outdoor localization system based on active tags will periodically emit signals with their IDs. off-the-shelf active RFID system and a probabilistic localization algorithm. Considering the uncertainty caused by the varying environments, Scout incorporates a probabilistic scheme based on on-site calibration and Bayesian inference to improve the localization accuracy. Bayesian inference was also used for cellular and WLAN-based localization systems [6, 7]. To the best of our knowledge, Scout is the first outdoor localization system integrating active RFID technology and probabilistic localization algorithm. Figure 1. Scout system structure The rest of this paper is organized as follows: Section 2 describes the Scout system, including the RFID readers: The whole detection area is covered basic system assumptions, system overview, and the with a RFID readers network such that any object tag probabilistic localization algorithm. In Section 3, the located in the area should be within the read range of at system performance metric is introduced, followed by least three RFID readers. The problem of reader the simulation results and related discussions. Section 4 deployment to satisfy this requirement has been concludes the paper. comprehensively studied in the literature, such as in [9]. The reader location is fixed and known. A reader is 2. Scout System responsible for 1) Collecting and decoding the signals emitted by the active tags within its coverage, 2) The Scout system uses off-the-shelf long range Measuring the RSSI for each tag within its range, 3) active RFID systems, similar to the Mantis system [1, Reporting tag ID, corresponding RSSI, and its own ID 2]. According to [2], this system works at 433MHz number to the server. To realize the above frequency range, with a minimal range of 100 meters functionalities, each RFID reader is equipped with two and maximum range of 500 meters. The MANTIS interfaces: a RF interface that detects RFID tags within reader can provide Received Signal Strength Indicator its coverage; and a communication interface (Ethernet (RSSI) readings for each tag within its range. In the or 802.11b wireless interface) for communicating with next section, we discuss the network layout of the the servers. Scout system. Servers: Each RFID reader should be within the reach of at least one server, so that the readers can 2.1. Network Layout communicate the measured location information of the tags to the servers. The number of servers deployed in Scout system consists of an RFID sensing network a detection area depends on the size of the detection that measures the RSSI information from the objects area and the system design. A server is responsible for (or active tags), and a communication network that 1) Collecting location information sent from the enables the transmission and processing of the location readers within its coverage area, and 2) Calculating the information. The Scout system follows a hierarchical location of the object tags according to the localization architecture comprising of the following three tiers: algorithm. Servers, RFID readers, and RFID tags. For simplicity, The Scout system works as follows: After the in Fig. 1, we just show the hierarchical architecture system is setup, the RFID readers will begin to detect within the coverage of one server, although in practice the signal sent by RFID tags. If any tag is located the whole detection area may be covered by several within its read range, the reader will collect and report servers. the identification and the RSSI of the tag along with its RFID tags: Two types of active tags are included in own ID to the corresponding server. All the the system, object tags and reference tags. Every information reported by the RFID readers will be tracked object will be attached with a unique active processed at the server(s). The server(s) will estimate RFID tag, called “object tag”, used for identifying and the location of the objects (to the readers) according to tracking the object. The ID of an object tag is a unique the Scout localization algorithm using the reported RSS identifier for a tracked object. So, “object tag” and information and the position of the readers.
- 3. environment, such as shadowing. Thus the path loss 2.2. Probabilistic Localization Scheme can only be considered as an average value. To take these factors into consideration, it has been shown that The proposed algorithm estimates objects’ locations the received signal strength usually demonstrates a based on RSSI of the object tags and the reference tags Gaussian normal distribution [8]. Hence, we take a provided by the readers. However, signal strength probabilistic approach and model the path loss at based RF localization systems suffer from the inherent distance d as a random variable PL(d) by using a errors associated with the RF propagation due to Gaussian random variable N [0, 2 ] with zero-mean, factors such as fading, shadowing, interference, etc. and standard deviation- . These factors cause random variations of the received d PL ( d ) PL ( d ) N [0, ] PL ( d ) 10n log( ) N [0, 2 ] (2) signal strength (RSS), and thus make it impossible to 0 d0 come up with a deterministic relationship between the With a given transmitting antenna power Pt , RSSI and the propagation distance. The proposed Scout algorithm is robust to transmitting antenna gain Gt , and receiving antenna randomness caused by propagation variations. It adapts gain G r , the received signal strength Pr ( d ) (in dB) at to varying outdoor environmental conditions by following the subsequent steps: 1) Calibrates the distance d is given by: propagation parameters using on-site reference tags, 2) Pr (d ) Pt Gt Gr PL(d ) (3) 2 Estimates the distance between the object and the Pt Gt Gr PL(d0 ) 10n log(d ) N [0, ] readers based on a probabilistic model deduced from Because the first four terms in (3) are all constant, general RF path loss model, and 3) Determines the we can use one constant to represent them. So the target location by applying Bayesian inference. above equation can be rewritten as: The Scout localization algorithm is based on the Pr (d ) Pr (d ) N (0, ) 10n log(d ) N (0, 2 ) (4) following assumptions: 1) Any object located in the area can be detected by at least three RFID readers, 2) where is a constant. It equals the median signal The readers are fixed, and their locations are known, 3) received at d 0 1m . There is one reference tag per reader deployed in the From (4), we can see that given the actual distance reader’s vicinity, and the location of the reference tags d PHY between the tag and the reader, the received is known, 4) The transmission power of all tags is signal strength Pr (d PHY ) is a Gaussian random variable identical. 2 In the following subsections we introduce the with mean 10n log(d PHY ) and variance . So the probabilistic model, the calibration of the propagation probability density function (PDF) of observing a parameters, and the Bayesian inference based certain value of Pr (d PHY ) (i.e, RSSI reported by the localization algorithm. reader) is given by: 2.2.1. Probabilistic Model 1 (RSSI 10n log(dPHY ))2 (5) p(P (dPHY ) RSSI ) r exp In order to estimate the distance between a 2 2 2 transmitter (i.e., tagged object or reference tag) and a Or given the actual distance d PHY between the tag receiver (i.e., reader), we use the log-distance path loss model that has been used extensively in the literature and the reader, the PDF of the estimated [8]: distance, d estimate , is given by following the usual PL(d ) PL(d0 ) 10n log(d / d0 ) (1) transformation of variable approach: where d represents the distance between the transmitter (RSSI ) 1 (RSSI 10n log(dPHY ))2 p(destimate ) exp and receiver, PL(d ) is the median propagation loss (in (destimate ) 2 2 2 (6) dB) measured at distance d; n is the path loss exponent 10n (10n log(destimate / dPHY ))2 exp which indicates the decreasing rate of signal strength in destimate ln10 2 2 2 an environment; d0 is a reference distance normally ( RSSI ) where d estimate 10 10 n (7) chosen close to the transmitter. In general, the exponent n is environment-dependent. In free space, n is equal to The PDF of d estimate is plotted in Fig. 2. From Fig. 2, 2. In more complicated environments, n will generally we observe that as d PHY (actual distance in the figure) be larger. Note that the model introduced in (1) does not or (v in the figure) increases, or as n decreases, the consider the variable factors in the surrounding spread of the PDF become flatter, and the uncertainty
- 4. associated with the propagation increases (in the figure, the following equations: the unit for the estimated distance is meter). n j (i ) nj ; 2 i 1,2,3 i j (9) RSSI j ( j )- RSSI j (i ) n j (i ) ;(i j) dist (i, j ) 10log( ) dist(j , j ) j (i ) j ; (10) i 1,2,3 2 i j j (i ) RSSI j (i ) 10n j (i ) log(dist (i, j )); (i j) The standard deviation of detection area can be calculated by: Nr No Figure 2. PDF of the estimated distance 1 (11) [ std ( RSSI j (i ))]2 Nr No j 1 i 1 2.2.2. Calibration of the Propagation Parameters where N r and N o denotes the number of the readers and In order to calculate the probability using (5), or (6) the tags respectively. For the above example and (7), we need to know the value of the propagation scenario, N r 3, N o 4 ; std ( RSSI j (i )) is the standard parameters: , n , . In Scout localization scheme, we use on-site reference tags and object tags to calibrate deviation for vector [ RSSI 1j (i), RSSI 2 (i),....RSSI w (i) ]. j j the propagation parameters during the process of localization. As assumed before, every reader has a 2.2.3. Bayesian Inference based Localization corresponding reference tag deployed in its vicinity. Algorithm The reference tags are static and their location is Notations and definitions: known. After detection, the readers report the RSSIs of 1) Basic detection area: The area overlapped by the both the reference tags and the object tags. This section range of three adjacent readers (Fig. 3). describes how the Scout algorithm calibrates the propagation parameters. Let us consider the scenario depicted in Fig. 3. There are 3 readers, reader_1, reader_2, and reader_3 for a specific area. Tag_1, tag_2, tag_3 are the reference tags corresponding to the three readers and tag_4 is the tracked object. Suppose during a period of time, time slot 1 to time slot w, the three readers detect w times (one detection during each time slot) and report the tags’ signal strength as {RSSI tj (i)} , (j=1,2,3; i=1,2,3,4; Figure 3. Grid and basic detection area t=1,2,3,4….w). Here, RSSI tj (i ) denotes the signal strength of tag_i reported by reader_j at detection time t. If w is large enough, then from (4): w RSSI (i) j RSSI t (i)) / w P (dist (i, j)) j r j 10n log(dist (i, j)) (8) j t 1 where RSSI j (i ) is the average received signal strength of tag_i reported by reader_j calculated from the measurements obtained during w slots; dist (i, j ) denotes the Euclidean distance between reader_j (j=1,2,3) and tag_i (i=1,2,3 for reference tags, 4 for the tracked object); j , n j denote the propagation constant Figure 4. Observations of an object and the path loss exponent of reader_j, respectively. 2) Grid cell: The detection area covered by a server According to (8), the propagation constant and the is divided into a uniform grid (Fig. 3). Each square path loss exponent of reader_j can be calculated using patch is called a grid cell, the center of a grid cell is
- 5. called grid point, and the distance between two nearest RSSI for reader_j as: t grid points is called grid_step. Assume N grid cells 1 (13) RSSI tj RSSI ij exists in the detection area, the grid cells are denoted wi t w 1 by C( gx , gy ) , C( gx , gy ) ,…., C(gx ,gy ) , using the coordinates of We denote these three readers by reader_1, 1 1 2 2 N N their grid points ( gxk , gyk )(k=1,2,…N). reader_2, reader_3. Then, a new observation matrix which includes only the observations from these three 3) State of an object at time t, x t : The state is the readers will be presented as: tracked object’s location. In our grid-based localization RSSI1t w 1 RSSI1t w 2 ... RSSI1t (14) system, we use grid cells to denote the location, so we [RSSI 't w 1 , RSSI 't w 2 ,...RSSI 't ] t RSSI 2 w 1 t RSSI2 w 2 t ... RSSI2 t w 1 t w 2 t represent the state of the object at time t by discrete RSSI3 RSSI3 ... RSSI3 random variable x t , xt {C( gx , gy ) , C( gx , gy ) ,...C( gx , gy ) } . 3) Calibration of the Propagation parameters: We 1 1 2 2 N N 4) Observation RSSI : RSSI is the RSSI of the t t use the method described in section 2.2.2 to calibrate j j the propagation parameters of the three readers in the object (or reference tag) reported by reader_j at time t. basic detection area. Given location of the three readers Fig. 4 shows the sequence of RSSI tj (j=1,2…R, and the corresponding reference tags and the t=1,2,…T) from R readers located within the server’s observations falling in the current window, the coverage during time interval [1, T]. propagation parameters can be calculated by (9) and 5) Belief Bel ( xt ) : At time t, a probability (10); Given [ RSSI 't w 1 , RSSI 't w 2 ,...RSSI 't ] of the distribution over possible value of x t , called Belief, reference tags and the tracked objects, the propagation standard deviation can be calibrated by (11). represents the uncertainty. Bel ( xt C( gx , gy ) ) represents k k 4) According to the observation vector at time the probability that the object is located within grid cell t, RSSI 't , we can estimate the object’s location using C( gx , gy ) at time t. This probability will be computed by k k Bayesian inference. using a series of observations. The Bayesian inference is used in the following 6) Window_size, w: The number of observations way: Whenever the sensors (i.e., the readers) provide a per reader that will be used for calculating Bel ( x t ) in new observation at time t, RSSI 't , the server first one recursion (Fig. 4). predicts state at time t according to the state at time t-1: 7) Recursion_time, r: The number of times we Bel ( xt ) p ( x t | xt 1 ) Bel ( xt 1 )dxt 1 (15) execute the recursive Bayesian inference to estimate the location of the tracked object. where x t 1 represents state of the object at time t-1, 8) Estimated Area Threshold, P: The estimation Bel ( xt ) is called predictive belief, p( xt | xt 1 ) is the area is given by the grid cells with belief higher or system dynamic model. Then the server corrects the equal to P. predicted belief using the observation at time t by: Input: The tracked object ID, location of the Bel ( x t ) t p ( RSSI 't | x t ) Bel ( x t ) (16) readers ( x j , y j ) , reference tags (rxi , ryi ) , and grid points where p( RSSI 't | xt ) is the perceptual model which ( gxk , gyk ), threshold P, observations for the object and describes the likelihood of having observations reference tags collected in interval [t-w+1, t] given by: RSSI 't given that the object is at location x t , t is a RSSI1t w 1 RSSI1t w 2 .... RSSI1t (12) constant used to normalize the resulting Bel ( xt ) . RSSI 2 w t 1 RSSI 2 w t 2 ... t RSSI 2 . . . . Implementing Bayesian filters requires specifying [ RSSI t w 1 , RSSI t w 2 ,...RSSI t ] . . . . the perceptual model p( RSSI 't | xt ) , system dynamics . . . . model p( xt | xt 1 ) , and the representation of the RSSI R w t 1 RSSI R w t 2 ... t RSSI R belief Bel ( xt ) . Output: estimated area of the object. Procedure: 4a) System Dynamics Model p( xt | xt 1 ) : 1) Initialization: The initial belief is given by: For moving objects, especially fast moving object, 1 1 1 this model depends on the object motion parameters, Bel ( x 0 ) { , ,... } . N N N such as direction, speed, etc. t t 1 t t 1 2) Determine the basic detection area: Use the For the static objects, p( x | x ) 1 if x x . observations of the object as defined in (12). The basic p( xt | xt 1 ) 0 xt xt 1 detection area is determined by the three readers with So, according to (15), we have: the strongest average RSSI. We define the average Bel ( x t ) p ( x t | x t 1 )Bel ( x t 1 ) dx t 1 Bel ( x t 1 ) (17)
- 6. 4b) Perceptual Model p( RSSI 't | xt ) : Euclidean distance between the object and the center of For a single reader, for example reader_1, we use the cell (i.e., the grid point corresponding to the cell): the model described in Section 2.2.1. The PDF given E (C( gxk , gyk ) ) dist (( gxk , gyk ), ( x0 , y0 )) (20) by (5) is used to calculate p1 ( RSSI1t | xt ) , reader_1’s ( gxk x0 )2 ( gyk y0 ) 2 perceptual model at time t. Combining the observations where ( gxk , gyk ) are the coordinates of the grid point, from m readers to estimate the object’s location, we use the following equation: ( x0 , y0 ) are the coordinates of the object, and dist ( ) m (18) is the Euclidean distance between two points. p( RSSI 't | xt ) p j ( RSSI tj | xt ) j 1 As described in Section 2, the Scout localization where p j ( RSSI tj | xt ) represents the perceptual model of algorithm is a probabilistic localization algorithm, which uses beliefs to represent the probability that the a single reader, reader_j. For our algorithm, we use object is located within a given grid cell. Therefore, the m=3. error distance is a discrete random variable. Given the 4c) Calculating belief Bel ( xt ) : estimated area A, the AED is given by: We calculate the probability belief Bel ( xt ) using E (C( gxk , gyk ) ) Bel ' ( x t C( gxk , gyk ) ) (16). Bel ( xt ) is obtained by (17) and p( RSSI 't | xt ) is AED k Bel ' ( x t C( gxk , gyk ) ) (21) obtain by (18), t is a constant for time-t which is used k to normalize the resulting Bel ( xt ) . In our algorithm, we ( gxk x0 ) 2 ( gyk y0 ) 2 Bel ' ( x t C( gxk , gyk ) ) k use grid point ( gxk , gyk ) to calculate the Bel ' ( x t C( gxk , gyk ) ) belief Bel ( xt C( gxk , gyk )) . k where C( gx , gy ) A ; and Bel ' ( xt C( gxk , gyk ) ) is the 5) Determination of the resulting estimated area: k k After r times of Bayesian inference, the resulting relative belief of grid cell C( gx . k , gyk ) estimated area is decided by the relative belief, Bel ' ( xt ) : As defined above, AED is the average error Bel ( xt ) (19) distance between the object and the grid points within Bel ' ( xt )= | max (Bel ( xt C( gxk ,gyk ) )) | the estimated area. AED reflects the localization k 1,2.. N accuracy of the Scout algorithm. Large AED values where N is the number of the grid cells in the detection mean poor accuracy while small AED values reflect area. Given threshold P, the resulting estimated area high localization accuracy. The minimum value of comprises of the states with relative probability belief AED is zero, which will happen when there is only one larger or equal to P. cell in the estimated area and its grid point is exactly where the object is. The maximum value of AED 3. Simulation Results increases with the distance between two adjacent readers. In this section, we evaluate the performance of the proposed localization scheme. First, we will develop a 3.2. Visual Results performance metric for Scout localization accuracy. Then, we illustrate visual results that depict the We use MATLAB to simulate a basic detection resulting estimated areas for multiple objects. At last, area covered by reader_1, reader_2, and reader_3. Five we investigate the scheme performance dependence on tracked objects are distributed within the area, as a number of system, algorithm, and environmental shown in Fig. 5(a). All the tracked objects are static. In parameters. the simulation, we consider the environmental factors such as shadow and fading, which cause received 3.1. Performance Metrics signal strength measurement errors (refer (4)). The Gaussian random variable in (4) will be generated To characterize the localization accuracy of the randomly using MATLAB randn function. The proposed Scout algorithm, we define a performance simulation parameters are: Grid_step=1, window_size metric denoted as the Average Error Distance (AED). w=20, recursion_time r=10, path loss constant 0, The output of the localization algorithm is an path loss exponent n=4, distance between two adjacent estimated area that is composed of several grid cells as readers D=80m, RSSI standard deviation 3 dB defined in Section 2. We define the Error (according to [1], 3 dB is a typical standard variance Distance, E (C( gx , gy ) ) , for grid cell C ( g x , g y ) , as the for the long range active RFID systems). Fig. 5(b)-(f) k k k k
- 7. depict the resulting estimated areas for each object for distance between the object location and the estimated different values of threshold P, P=1, 0.75, 0.5. Each location corresponding to P=1 are less than 2 meters estimated area includes all the grid cells which have for all the five objects. Furthermore, we observe that relative belief probability equal to or larger than the the shapes of the estimated areas are decided by the threshold P. The solid line and the dash line depict the objects’ distances from the three readers. The closer corresponding estimated area of P=0.75 and P=0.5 readers have more influence on the results. For respectively. example, (see Fig. 5(f)) object_5 is located very close to reader_2 while very far away from the other two readers. So, the nearest reader, reader_2 dominates its estimation result, and the estimated areas are ring shaped around reader_2. Object_3 is in a similar situation with respect to reader_1. 3.3. Numerical Results We conduct a series of experiments to evaluate the performance of the Scout localization algorithm. We (a) Topology (b) Object_1 (0, 23) use MATLAB to simulate the impact of the following parameters on the localization accuracy represented by Average Error Distance (AED): window_size (w), recursion_time (r), distance between two adjacent readers (D), RSSI standard deviation ( ), and Path Loss Exponent (n). The default topology is shown in Fig, 5(a). The received signal strength at a certain distance from the reader is simulated as a Normal random variable (refer to (4)). (c) Object_2 (0,0) (d) Object_4 (10, 40) 3.3.1. Impact of window_size and recursion_time Experiment 1: The simulation is based on the following system configuration: grid_step=1, threshold P=0.5, D=80m, n=4; =0; =3dB. We run the simulation for the following three scenarios: 1) ideal calibration, i.e. the propagation parameters (n, , ) obtained from the on-site calibration procedure are identical to the real environmental parameters; 2) window_size w=20;and 3) window_size w=2. (e) Object_3 (0, 65) Fig. 6(a) depicts AED as a function of the recursion_time for the three scenarios. We observe that AED decreases as we increase the recursion_time. Moreover, we notice that the window_size has limited influence on the accuracy of the results. This is due to the fact that the use of the recursive Bayesian inference will eliminate the uncertainty caused by the limited sampling. Fig. 6(b) illustrates AED as a function of the recursion_time for different objects in the above first (f) Object_5 (-35,10) scenario. We observe that AED convergence speed is a Figure 5. Visual results function of the object locations within the basic detection area. For object_1 and object_2, the The results demonstrate that the proposed algorithm localization accuracy increases much faster than that of works well for all the five objects. It can effectively object_3, with increasing of the recursion_time. From estimate the locations of the tracked objects. As shown (18) we can see that Bayesian inference makes use of in the figures, all the five objects are within the the observations from the three readers to recursively corresponding estimated area of P=0.75. And the diminish the uncertainty caused by the RF propagation.
- 8. Furthermore, from (6) and its plot, we learn that for the readers. We notice that as the distance between the distances far away from reader, the PDF curve becomes readers increases the localization accuracy, expressed flatter and the uncertainty increases. Since object_3 is in terms of AED, decreases. very far from reader_2 and reader_3, the observations From the probabilistic model presented in (6), we from reader_2 and reader_3 contain huge uncertainty observe that increase in d PHY (the physical distance and provide little information. This is equivalent to the between the object and the reader) increases the spread situation that Bayesian filter only make use of of the PDF, increasing the localization uncertainty observation from reader_1 for localizing object_3. which decreases the estimation accuracy. While for the object_1 and object_2, the observations D and ADE Average Distance Error (meters) 4 from all the three readers provide useful localization Object 1 information. As a result, the localization of object_3 3.5 Object 2 converges very slow comparing to object_1 and 3 object_2. 2.5 2 1.5 1 50 60 70 80 90 100 110 120 Distance Between Readers, D (meters) Figure 7. AED vs distance between readers, D 3.3.3. Impact of Propagation parameters, , n Experiment 3: In this experiment we plan to evaluate the impact of the propagation standard deviation on the localization accuracy. We perform the simulation for object_1, object_2, and object_3 depicted in Fig. 5(a) using the following system configuration: Grid_step=1, D=80m, w=20, r=10, n=4, 0 , P=0.5. Fig. 8(a) plots AED as a function of the standard deviation . Experiment 4: In this experiment we evaluate the impact of the path loss exponent n on the localization accuracy. We perform the simulation for object_1, object_2, and object_3 depicted in Fig. 5(a) using the following system configuration using the following system configuration: Grid_step=1, D=80m, w=20, r=10, 0, =3dB, P=0.5. Fig. 8(b) plots AED as a function of the path loss exponent. Figure 6. Impact of window_size and recursion_time From Fig. 8(a) we observe that AED increases (i.e. on the system performance the localization accuracy decreases) as the standard deviation increases. From Fig. 8(b) we conclude 3.3.2. Impact of the distance between two adjacent that AED decreases (i.e. the localization accuracy readers increases) as the path loss exponent, n, increases. From Experiment 2: Assume the distances between the the probability model defined in (6) and (7), increase readers are the same. We perform the simulation for the in or decrease in n, increases the spread of the PDF, object_1 and object_2 with the same geometry but increasing the uncertainty associated with the different scale. As depicted in Fig. 5(a), Object_1 is propagation which results in decrease of the located at the gravity center of the basic estimated area localization accuracy. and the object_2 is located at the midpoint of reader_2 Moreover, from Fig. 8(a) and (b) we also observe and reader_3. To eliminate the impact of inaccuracy that the AED increase rate depends on the relative caused during propagation parameter calibration, the position of the object to the readers. The change of the simulation is run for the ideal calibration scenario. The propagation parameters has the smallest impact on system configuration is set as following: Grid_step=1, object_3 and the largest impact on object_1. Since w=20, r=10, n=4, 0 , =3dB, P=0.5. Fig. 7 object_3 is dominated by reader_1, only change of the illustrates AED as a function of the distance between
- 9. reader_1’s measurements (caused by the change of the In this section, we test the system localization propagation parameters) influences object_3 performance using the following experiment. localization. Since object_1 is located equidistant from Experiment 5: This experiment is designed to the three readers, object_1 localization is influenced by probabilistically evaluate the system performance changes of all the three readers’ measurements. under the following settings: Using the same reader network topology as shown in Fig. 5(a), we track 1000 objects which are uniformly distributed within the basic detection area. The other simulation parameters are: Grid_step=1, D=80m, n=4, 0, 3 dB, P=1, 0.8, 0.6. We test the system performance for three scenarios: 1) w=20, r=10; 2) w=20, r=20; 3) ideal calibration, r=10. Fig.9 shows the cumulative Average Error Distance distribution obtained from scenario 1 and 2. We notice that over 90% percent of the object the localization AED falls within 7 meters and 5 meters for the scenario of r=10 and 20 respectively. As expected, increasing recursion_time r will leads to higher localization accuracy. Fig.10 shows the cumulative Average Error Distance distribution obtained from scenario 1 and 3. As mentioned before, increasing w will make the system performance incline to that of ideal calibration. So, Fig. 10 shows the upper bound of the system performance for r=10. Furthermore, from Fig. 9 and Fig. 10, we can get the conclusion that in most of the situation, localization accuracy with larger P (P<=1) is better. Figure 8. Impact of the propagation standard deviation and the path loss exponent 3.3.4. System Performance CDF of AED 1 0.9 r=10,P=1 0.8 r=10,P=0.8 r=10,P=0.6 0.7 r=20,P=1 Cumulative Percentage r=20,P=0.8 0.6 r=20,P=0.6 0.5 0.4 0.3 0.2 0.1 0 0 1 2 3 4 5 6 7 8 9 10 11 12 Average Error Distance (meters) Figure 9. AED distribution for r=10 and r=20
- 10. CDF of AED 1 0.9 0.8 w=20,P=1 0.7 w=20,P=0.8 Cumulative Percentage w=20,P=0.6 0.6 ideal, P=1 ideal, P=0.8 0.5 ideal, P=0.6 0.4 0.3 0.2 0.1 0 0 1 2 3 4 5 6 7 8 9 10 Average Error Distance (meters) Figure 10. AED distribution for ideal calibration and w=20 4. Conclusion [2] Mantis: http://www.rfcode.com/data_sheets/mantis.pdf [3] J. Hightower, C. Vakili, G. Borriello, and R. Want, “Design and calibration of the SpotON Ad-Hoc Location In this paper, we presented Scout, a cost-effective Sensing System,” UW CSE 01-08- University of outdoor localization system based on active RFID Washington, Seattle, WA, August 2001 systems as well as a probabilistic approach to the [4] A. Sayed, A. Tarighat, and N. Khajehnouri, “Network- localization problem using only RSSI readings. Scout based wireless location,” IEEE Signal Processing Mag., system relies on a hierarchical architecture to cover a vol. 22, no. 4, pp. 24–40, July 2005. large outdoor environment. The proposed approach [5] L. M. Ni, Y. Liu, Y. Cho Lau, A. P. Patil, addresses the localization uncertainty due to the “LANDMARC: indoor location sensing using active varying environments. Using MATLAB based RFID”, In Proceedings of the First IEEE International simulations we have evaluated the performance of the Conference on Pervasive Computing and Communications (PerCom 2003), 23-26 March 2003 proposed approach. For the example system settings, [6] T. Roos, P. Myllymaki, and H. Tirri, “A Statistical the simulation results show that in 90% percent of the Modeling Approach to Location Estimation,” IEEE localization estimation, the system provides objects Trans. on Moible Computing, vol.1, Issue 1, pp 59-69, location with a AED (Average Error Distance) less Jan. 2002 than 7 meters. Also, the simulation shows that the [7] T. Roos, P. Myllymaki, H. Tirri, P. Misikangas, and J. system performance improves with the higher values Sievanen, “A probabilistic Approach to WLAN User of recursion_time, window_size, reader density, path Location Estimation,” International Journal of Wireless loss exponent, and with lower values of the Information Networks, 9 (3), July 2002 propagation standard deviation. The simulation results [8] T. S. Rappaport and T. Rappaport, Wireless Communications: Principles and Practice, 2nd Edition. prove that the proposed system is an accurate and cost- Pretice Hall, Dec.2001 effective candidate for outdoor localization. [9] C.-F. Huang and Y.-C. Tseng, “The coverage problem in a wireless sensor network,” ACM Int’l Workshop on 5. References Wireless Sensor Networks and Applications (WSNA), pages 115-121, 2003 [1] BSR INCITS 371.2, Real Time Location Systems (RTLS) – Part 2: 433-MHz Air Interface Protocol”.

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