Ground Target Tracking of Vehicles in a Wireless Ground Sensor Network

Transcription

1 Ground Target Tracing of Vehicles in a Wireless Ground Sensor Networ Mats Ean Security and Defense Solutions Saab AB Järfälla, SE-75 88, Sweden ats.x.ean@saabgroup.co Henri Pålsson Security and Defense Solutions Saab AB Järfälla, SE-75 88, Sweden henri.palsson@saabgroup.co Abstract In this paper data fro a wireless unattended ground sensor networ is used for tracing vehicles travelling in a road networ. A ultiple odel particle filter algorith, taing into account the road inforation, is studied. The algorith enables tracing of targets driving both on and off road. In the PF algorith two different dynaic odels, a second order linear Gaussian odel and a constant velocity odel, are used to represent the on and off road otion. In order to handle realistic ground target tracing scenarios, the tracing algorith is integrated into a ultiple-sensor tracing syste platfor which is able to handle real data fro a wireless ground sensor networ. The perforance of the ground target tracing algorith is evaluated using real data together with siulated data. Keywords-Particle Filter; Wireless Ground Sensor Networ; Ground Target Tracin; IMM algorith I. INTRODUCTION It is often hard to both detect and continuously trac targets in ground target surveillance applications since e.g. obstructing terrain iposes liited range of the sensors. The types of sensors used in ground target tracing applications often iply difficulties in distinguishing targets fro the bacground, especially if the targets are not oving. For instance, acoustic sensors often eet with probles in discriinating the sound fro relevant targets fro the bacground noise. One possible way of overcoing the proble of aintaining continuous tracing is to integrate surrounding higher level inforation in the tracing algorith. An obvious source for this ind of inforation is digital ap inforation. Utilizing sequential Monte Carlo (SMC) techniques for ground target tracing has shown proising results, see e.g. []-[4]. Soe of the ground target tracing scenarios in the literature include road networs. In this paper the tracing of ground target vehicles using real data fro a wireless unattended ground sensor networ (UGSN), including acoustic sensor data, is studied. The tracing solution to the proble is given within the Bayesian recursive fraewor. A odified version of the ultiple lielihood odel particle filter (MLM- PF) algorith, given in [4], is used in order to efficiently incorporate road inforation in the tracing algorith. Different target dynaic odels represent the on and off road otion of the targets - a second order linear Gaussian odel is operating for the on road otion and a standard constant velocity odel is acting when the target is leaving the road. The ixing technique in the MLM-PF is based on the sae idea as in the well-nown interacting ultiple odel (IMM) filter, see further [5]. The tracing PF algorith is integrated in a ultiple-sensor (MST) tracing syste, adapted for ground target surveillance applications. The MST systes ipleentation has the sae basic eleents as in conventional MST systes (see e.g. [6]). This includes ulti-target tracing, initiation, confiration, erging, pruning of tracs as well as gating functionalities. The approach for solving the data association proble is based on hypothesis calculations according to the oint probabilistic data association (JPDA) ethod; the version used here is described in [7]. In addition there also exist special algoriths for the incorporation of digital road inforation. The digital ap inforation is priarily stored in so-called Shape files, which are not suitable to use directly in the MST syste. Hence, the digital road inforation needs to be prepared and re-stored before it is incorporated into the tracing algorith. All together this results in that the MST syste platfor can efficiently handle real ground sensor easureents generated by a prototype UGSN, see [8] for a detailed description of the UGSN. This wor will focus on the novelty in the odified MLM-PF algorith, where especially the incorporation of road networ inforation and the usage of two dynaic odels in a ultiple odel PF fraewor are iportant. This paper is organized as follows: In Section II the UGSN is briefly presented. Section III describes the tracing syste, where focus is on the digital road inforation ipleentation. Section IV develops the MLM-PF algorith. The ground target scenario together with discussions on the results fro the study are given in Section V. Finally, concluding rears are presented in Section VI. II. UNATTENDED GROUND SENSOR NETWORK The priary goal for the UGSN is to detect and trac vehicles oving within the area covered by the UGSN. The prototype UGSN utilized in this wor consists of at ost nodes and central nodes. The central nodes act as obile control sites where data fro all nodes are collected, processed and displayed. All sensor data can also be recorded in the central node, which enables off-line evaluation of the UGSN and the tracing behavior. The ground sensor networ is coposed of a nuber of sensors, each associated with a node 39

2 with transission capability. The counication between each node is enabled since the networ is based on IEEE8.b standards (cards). An ad-hoc architecture is used to counicate easureents between the nodes. The UGSN has the capability to process and trac targets at each node to get a osaic picture. However, in the set-up utilized in this wor the UGSN is used as a distributed networ, where inforation is relayed through the nodes to a central fusion site. The different sensor types that can be connected to a node are found in Table. In this wor ost of the sensors in the USGN are the acoustic sensors shown in Fig.. The acoustic sensors are prototypes developed by the Swedish Defence Research Agency (FOI). The sensors have three icrophones arranged in a circular array with degrees of spreading. Fro this icrophone array a bearing to the target is estiated by studying the cross correlations between the easured icrophone signals. TABLE I. SENSOR TYPES Sensor Type Nuber Manufacturer Developed by FOI Thales Magnetic Thales PIR (Passive IR) 4 Thales Radar SIRS () Saab Bofors Dynaics Analog Video Malux Figure. sensor array developed by the Swedish Defence Research Agency (FOI). Three icrophones for a circular acoustic array, with a radius of 5 c. III. MULTI SENSOR TRACKING PLATFORM In this wor the experients are perfored on a prototype Matlab version of a MST ipleentation of the ground target tracing and surveillance algoriths. The purpose of the ipleentation is to build an environent where the core algoriths are based on SMC ethods and where different ground target scenarios, using both real data and siulated data, easily can be included and tested. Thus, the prototype Matlab MST contains the sae basic eleents as in conventional MST systes. However, the filtering and prediction eleents are based on SMC ethods. Moreover, using SMC ethods soeties iply that novel logics in eleents of the MST syste need to be developed. In Fig., a siplified scheatic picture of the eleents in the MST syste is shown. Gating Observation to Trac Association (PF-JPDA) Initiation Sensor Data Tentative Tracs Confired Tracs Trac Confiration Trac Pruning Trac Merging Road Map Calculations Filtering/Prediction/Mixing Map Inforation Figure. Scheatic picture of the different eleents in the MST syste. A description of all eleents will be too extensive for this wor; instead we will focus on the incorporation of ap inforation. A ore detailed description of the filtering, prediction and ixing step of the MLM-PF algorith is given later on in Section IV. A. Incorporation of ap inforation The road ap calculation eleent in Fig. is a part of the integration of road ap inforation in the tracing algorith. This eleent contains functions that fro a position and a distance of an on-road particle, calculate a new position according to the road networ. The eleent also includes functions that find distances to the end points of a road section. Special logics are handling the cases where a particle is at a crossing or at a dead end. The ap data used in the MST syste are originally represented in so-called Shape file forat. In Sweden, ap inforation is aintained by an authority naed Lantäteriveret (National Land Survey of Sweden). In this wor sall ap sections were supplied by the Swedish Defence Materials Adinistration (FMV). A Shape file includes a nuber of layers, each representing a certain terrain type, and an ach terrain layer contains a nuber of polygons that include the terrain in question. The roads are represented as polylines, but there is no explicit representation of road crossings. The ain idea in the odified MLM-PF described in Section IV is to use a one-diensional" on road odel (we refer to the diension of a odel in ters of the lateral position) where vehicles are only oving forward and bacward on the road segents. The forat for the roads in the native Shape files is one-diensional polylines. However, this forat is not convenient to directly incorporate into the PF fraewor. One reason is that the particle filter cannot easily handle road crossings. Thus, the representation of the ap data fro the Shape files is odified to be ore suitable for tracing. This odification is described next and has previously been exploited in [9]. A road is given by a nuber of road points, where a road segent connects two road points. Moreover, a road always 393

3 starts or ends at a unction. For instance, Fig. 3 shows five roads. repeated for all particles as illustrated in Fig. 5, where all possible noral vectors to a road segents intersecting a particle are displayed. Road segent Road points Road Figure 3. Representation of roads in the shape files. In the MST the Shape file forat is given a new representation, where each road is a structure containing all road points; see the exaple in Fig. 4. This new representation also allows crossings between the end points of the road. Figure 5. Norals to a road segents intersecting a particle. The road segents intersecting a particle are found by using vector calculations. Define A as the vector for the two road points P (, Y ) and P (, Y ), and vector B for the first road point P and the particle P 3 ( 3, Y 3 ). Then, it is possible to calculate how uch a proected vector, P, consists of vector A by the following calculations P B cos( v) A B u A A A A ( ( 3 )( )( ) + ( Y ) + ( Y 3 Y )( Y Y )( Y Y ), Y ) () where the following criterion for the particles can be identified Figure 4. Efficient representation of roads. The and Y fields are the (,Y)-coordinates for the road points, whereas the T fields list the category codes (i.e. type of road) and road nubers (e.g. E4 stands for European Route 4) according to the Shape file. The C fields indicate the oining road index nubers (i.e. the unctions) at each road point. Finally there is a string describing the road category. In this wor we use a ultiple odel setting, where on road and off road otions are possible. Hence, a one-diensional on road dynaic odel is utilized for the on road representation. This involves that particles propagated to a position on the field often have to be proected onto the nearest position on the road, which is quite coplicated, especially if considering the existence of crossings. In the sequel in this section we will denote the state vector for a particle i as x (i). An efficient proection function is utilized in the MST syste to handle proection of particles outside a road onto a specific road segent. Input to the function is a set, containing all state vectors x (i) for all particles, and the road networ N R. Output fro the function is a new set R and an additional atrix L which contain vectors l (i), for each particle i. The eleents in L contains road nuber, last passed point, direction, and inforation about the oveents of the particles. First the function finds all roads that are close enough to the involved trac. Then it tries to find all road segents which have their noral vectors intersecting a particle. This is u u < u > on road segent outside outside. If a road segent is found for the particle, then the new proected position (,Y) for the particle is coputed as the intersection point between the noral and the road segent according to () + u( ) (3) Y Y + u( Y Y ). There will be a nuber of road segents or points that the particle can be proected to. Obviously, it is only the road segent or the road point with the shortest distance that is of interest. Fast searching algoriths and gating are utilized in order to quicly find the shortest distance. Furtherore, in the algorith the particles are also updated with their corresponding road nuber and last passed road point. The function described in this sub-section is in the sequel referred to as the GetParticleOnRoad function. IV. MULTIPLE LIKELIHOOD PARTICLE FILTER, MLM-PF In [4] the MLM-PF was developed with the idea to let different odes be represented by different lielihood odels, 394

4 whereas the transitional density is assued to be the sae for all odels. However, in this application the different odes will not use the sae dynaic odel. Instead one dynaic odel represents the on road otion, and another dynaic odel the off road otion. The algorith is also suppleented with an extra ixing step copared to the original algorith. This additional ixing step is perfored previous to the propagation step, where particles are drawn according to the ode probabilities fro the old posterior density. This idea is also exploited in [9]. In the odified MLM-PF algorith, the following dynaic odel for the on-road otion is utilizing v + ζv + ω ( v vr ) cw, (4) where w is a zero-ean continuous-tie white noise of unit intensity, ω is the natural frequency, ζ is the daping ratio, t c ζ /ω is the tie constant and v R is the expected value of the vehicle speed. It is assued that ζ< and ω >. The constant c on the right side deterines the agnitude of the driving noise. This odel is also used for on road tracing of vehicles in [] and []. A discrete tie state space odel is obtained by sapling the state space odel version of (4), which gives r Fr + Gu + G v, (5) ω where r [s v a ] T, i.e. the eleents are the distance, velocity and acceleration, respectively. Moreover, u v R is the expected value of the vehicle speed, and v is a zero-ean discrete-tie white Gaussian process noise of unit intensity. The atrices in (5) are explicitly given by ζwt ζ ( ) sin( )... sin( )... ζ e T + ω e T + ζ ζwt + ( e cos( T)) + ( e cos( T)) ω ω ζω F e (cos( T) + sin( T)) e sin( T), ω ζω e sin( T) e (cos( T) sin( T)) ( ζω + ζe ω cos( T) +... ω ( ζ ω + ) 3 + ζ ω sin( ) + ζ ω + sin( )) (6) e T T T e T K ( e cos( T) ζωe sin( T)), ( ζ ω + ) sin( T) e G ω K, G ck, ω ζ. w In (6) the distance, s, is odeled as the distance of the target fro the last tie. The off road otion is represented by the standard discrete-tie constant-velocity (CV) odel x. (7) FCV x + GCV e The state vector x is the position of the target in Cartesian coordinates. Sipping the tie index we write this as x [ Y v v Y ] T, where and Y represent the particle position and v and v Y are the corresponding velocities. e is a white Gaussian process noise sequence with the variancesσ and σ along the Y and Y directions. The atrices in (7) are given by F CV T T, G CV T T T, T where T is the sapling tie. The sensor odel is given by (8) θ tan ( / ) Y h( x ), (9) ρ + Y for e.g. radar and seisic sensors. Only the first eleent in (9) is used for the bearings-only sensors (e.g. acoustic sensors). In the MLM-PF algorith all particles contain the state vectors in both (5) and (7), i.e. one vector for the onediensional dynaic odel and one vector for the twodiensional dynaic odel. For the one-diensional odel the distance s along the road curve is the position, whereas for the two-diensional odel the position is given by the (, Y)- coordinates. There are two reasons for having two state vectors in parallel. The first one has already been entioned; a particle ust be able to travel both on-road and off-road. The other reason is that particles have to be plotted on a ap. Hence, even if only the on-road odel in (5) is used in the tracing algorith it is convenient to also store the positions in the (, Y)-coordinates. To accoplish this, an algorith which aps the states, r p, is also utilized. This function calculates for each particle a new state x and a new inforation vector l fro the previous state x - and vector l -. We will refer to this function as the FindPoint function. Details of this apping algorith is given in [9]. Thus, a PF algorith for on road otion using the odel (5)-(6) has an additional step where the apping FindPoint is perfored. A sapling iportance resapling (SIR) version of an onroad PF is shown in Table II. The first step in Table II is the initialization of all particles, which are randoly distributed on the roads near the initial state x. The particles are thereafter proected onto the nearest roads using the GetParticleOnRoad function described in sub-section IIIA. After the initialization, the inforation about the location, i.e., the valid road nuber, the nearest corresponding road point and the direction, is stored in l (i) for each particle. This inforation is stored in the atrix L, and the on-road particles are stored in the set R. In the propagation step all particles on the road are updated by passing the particles through the odel (5)-(6). At the sae tie the state vectors in are updated using the function FindPoint described above. The last steps are the sae as in a standard SIR-PF: 395

5 TABLE II.. Initialize the particles, { R ( i) { x } N p i x ON ROAD SIR-PF ( x ), L} GetParticleOnRoad( and set.. Predict new particles using (5)-(6) and generate new apped particles according to, L } FindPoint(, L, R ). { 3. Measureent update: calculate the acceptance probability as in the standard SIR algorith. 4. Resapling: Generate a new set of particles using a standard resapling ethod. 5. Set + and iterate fro step. The on road algorith in Table II is partly utilized in the odified MLM-PF algorith. Since the particles on the road always have a two-diensional position, the ixing between the odels is straightforward to perfor. In the odified MLM-PF algorith the particles are propagated in each ode according to the corresponding dynaic odel. The iportance weights and the ode probabilities for each ode, as well as the ixing step are calculated in the sae anner as in the original MLM-PF in [4]. However, an additional ixing step is introduced before the propagation of the particles, where particles are propagated through the dynaic odel with the sae probability as for the ode probability. The probability for each ode is given by γ, p( y M, Y ) pi, γ i,,,,, (9) c i where y is the easureent at the current tie and Y - are all easureents up to tie -. Moreover, c is a noralizing constant and M is the ode at tie. The transitional probability, p i,, is defined as p i, P( M p i,, M i ), p i,, i,, ) i, M. () In (9) the conditioned lielihood is assued Gaussian as (, ) ( ˆ ) ( ; ( ˆ ), ˆ p y M Y l p y x N y h x S ), () where Ŝ is the innovation covariance atrix fro a filter estiate with odel. The sensor odel h( ) is a transforation fro a state vector to easureent values. As one input to the sensor odel we have a state estiate, xˆ. Here, we will use the Miniu Mean Square (MMS) estiate calculated fro the particle cloud as N x q~ ( i), ( i), ˆ x,,,, () i i where N is the nuber of particles in ode. q ~ ( ), and i x ( ), are the acceptance probability and the state, respectively, for particle i and ode. The innovation covariance atrix, Ŝ, can be calculated fro (). A generalized SIR-version of the odified MLM-PF is suarized in Table III and Table IV, where Table IV shows the ixing step. TABLE III.. Initialize the particles, SIR MLM-PF ( i), N { x } p i x ( x ),,, N ( i) N { x } { } i,,, and set.. Draw randoly N nuber of particles fro the set - -, according to the ode probability γ,-. 3. For,,, predict new particles, -, according to each dynaic odel. 4. Perfor the ixing described in Table IV, and obtain the cobined weights (i) q. 5. Resaple: Generate a new set, by resapling ( i) ( i), ( i) according to Pr( x x q. ) 6. Increase and continue fro step. TABLE IV. MIING STAGE FOR MLM-PF. Calculate the iportance weights ode.. Copute the estiates xˆ and i q ), ( for each Ŝ e.g. fro (). 3. For,,,, calculate the conditioned lielihood functions ( ˆ, ˆ l p y x S ). 4. For,,, calculate the ode probability γ,-. 5. Calculate the cobined weights according to ( i) ( i), q γ, q, i,, N. c N 396

6 V. The siulated part of the scenario is shown in Fig. 7, where sensors 3 8 are acoustic and sensor 79 is a -radar. The siulated part is connected to the live recording part before the data are feed into the MST syste. GROUND TARGET SCENARIO STUDY In order to investigate the feasibility of the developed algoriths and the MST syste for the land doain we have perfored live recordings on site with a USGN and a test vehicle equipped with GPS transponders. The scenario contains both live recordings fro the USGN and a siulated part. The live recordings were carried out on at Hoburg on the southern tip of Gotland. The ground sensor networ was deployed according to Fig. 6. Table V explains which and how the sensors were placed along the road. The sensors consisted of a -wave radar, acoustic bearing-only sensors, and seisic sensors. The ground sensor networ is coposed of a nuber of sensors, each associated with a node with transission capability as described in Section II Bilder Cnes/Spot Iage, Digital Globe, GeoEye, Lantäteriet/Metria, Kartdata Google 9 Figure 7. The siulated sensors. In the MST syste there are a nuber of eleents and functionality ipleented as described in Section III. For instance, the tracer is handling ulti-targets efficiently by a JPDA-PF technique described in [7]. However, in this study we will focus on the benefit of incorporating road inforation in vehicle tracing applications. The odified MLM-PF algorith is copared with a standard PF which is not taing into account the road inforation Bilder Cnes/Spot Iage, Digital Globe, GeoEye, Lantäteriet/Metria, Kartdata Google Figure 6. The live recordings with the USGN. The placeent of the sensor types along the road is shown in Table V. During the recordings for the real part of the scenario only one of the seisic sensors (sensor 36) was used. Hence, 7 acoustic sensors, -radar and seisic sensor are used for the recorded part of the scenario. TABLE V. Sensor Nuber Sensor SENSOR TYPES Sensor Sensor Radar 9 8 Relay 4 9 /Relay In the study 5 MC siulation runs are perfored using the recorded data and the attached siulated data. The nuber of particles are N, and the scenario lasts for 49 seconds in each run. The sae process noise paraeters, Q diag[.5.5], and initial covariance atrix, P diag[3 3..] for the off road odel, were used for both algoriths. For the MLM-PF algorith the ode transition atrix was chosen as Pon pi, Poff Pon, Poff (3) where Pon Poff.8. Moreover, the following constants for the on road odel (5)-(6) are used for all siulations; and t c 5T s, ω ζ / t c ζ.7, v R 3 / s, σ v.5, c σ v 4ζω 3, where T is the sapling tie. It is worth entioning that for the recorded data the sapling tie is asynchronous. However, for the siulated part the sapling interval is a constant T.3. Moreover, for the siulated part the accuracies of the -radar for the aziuth easureent is σθ. rad and for the range σρ 4. The aziuth accuracy is σθ.3 rad for the bearings-only easureents. The accuracies for the real sensors are approxiately σθ.35 rad for the acoustic sensors, and σθ. rad and σρ 4 for the -radar. The seisic sensor has a detection range of -6 for huan and 5-6 for vehicles. There 397

7 are a nuber of other paraeters that have to be set in the MST syste as well, for instance paraeters for the JPDA and gating functionalities, and also trac pruning and initiation paraeters. Those paraeters will not be described here, but they are the sae for the MLM-PF and the standard PF during the siulation runs. In Fig. 8 the result for the MLM-PF and the standard PF after 5 siulation runs are shown together with the true easureent for the vehicle. For the real data part the true position is the GPS position of the vehicle. The tracs are initiated after approxiately 75 seconds when the target is observed by the -radar. When the target passes the radar there are a nuber of acoustic sensors that start to detect the target. Here, it is iportant to ention that the acoustic sensors also give a vast nuber of false detections. After approxiately 3 seconds there is a large gap in easureents, until the seisic sensor (36) is detecting the target again after 85 seconds. The position for the seisic sensor can be seen in Fig. 6. The siulated part of the scenario starts after 3 seconds. There is a gap in easureents also in this part after approxiately 45 seconds. Then, the siulated -radar is detecting the target again after 435 seconds, and this sensor is observing the target until the end of the scenario. The target is leaving the road after 45 seconds and the target drives off the road in the reaining part of the scenario. Studying the results in Fig. 8 exposes that the tracing is uch ore stable if road inforation is incorporated in the algorith. For the standard PF there were also occasions where the trac was lost, i.e. the trac is deleted before the end of the scenario, since the trac quality has fallen below a threshold value. The poor results at the last stage of the scenario, where the target is leaving the road, reveal that at the end of the scenario soe of the tracs for the standard PF are actually lost, but not yet pruned. The ean value result for the MLM-PF is better, but also suffers fro the fact that in soe runs the trac stays at the road for too long tie. This phenoenon can be controlled by the paraeters in (3). Bilder Cnes/Spot Iage, Digital Globe, GeoEye, Lantäteriet/Metria, Kartdata Google Figure 8. Mean value of the traectories after 5 siulation runs. Solid reed is the traectory for MLM-PF, dashed blue is the PF and the dotted line is the ground truth. In Fig. 9 the RMSE for the 5 MC siulation runs is illustrated. The occasions where the tracs are lost are not included in the RMSE calculations. The result confirs that including road inforation stabilizes the tracing. It is also obvious that the tracing deteriorates when there is a gap in the easureents. However, the particle cloud for the MLM-filter, which taes into account the road networ, is ore spread along the roads when data are issing. This results in that the MLM-PF is rather quicly bac to trac when easureents starts to update the filter again, whereas the standard PF has soe difficulties to eep a continuous tracing. In Fig.9 this is ost obvious after the second occasion, where there is a gap in easureents, i.e. between 45 and 435 seconds. In any of the siulation runs the trac is not recovering again. The reason for the increase in RMSE for the MLM-PF after 45 seconds is that in soe siulation runs the trac, before it leaves the road, stics to the road for quite a long tie. It is also obvious that the standard PF deviates fro the true position when only one (bearings-only) acoustic sensor is updating the filter for a longer tie, which can be seen by the peas at 3, 33 and 37 seconds. 398

8 Figure 9. RMSE after 5 siulation runs. Solid reed is the RMSE for MLM- PF, dashed blue is the RMSE for the PF. One question that is especially iportant when road inforation is used in the PF is which single point estiate that should be used. This is illustrated in Fig. where a snapshot picture illustrates how the MLM-PF cloud is spread along the roads. This can happen for instance if the filter is deadreconing and the target is not observed for a while. In the figure the Maxiu A Posteriori (MAP) estiate is copared to the MMS estiate. The MMS estiate is found where noone would thin that the vehicle could be. In the calculations of the RMSE the MAP estiation technique described in [] is utilized. MAP True position MMS Bilder Cnes/Spot Iage, Digital Globe, GeoEye, Lantäteriet/Metria, Kartdata Google Figure. MAP versus ean estiates when particles are widely distributed in a road networ. VI. CONCLUSIONS In this paper we have developed a tracing algorith called a odified MLM-PF, provably capable of tracing vehicles on road and off road. The algorith is based on PF and IMM techniques and is efficiently utilizing ap data given in Shape forat. In the odified MLM-PF algorith two different dynaic odels, a second order linear Gaussian odel and a constant velocity odel, are used to represent the on and off road otion. The algorith is incorporated into a MST fraewor which is capable of tracing targets with real sensor data fro e.g. radars, acoustic sensors and seisic sensors. In this wor the sensor data eerge fro a prototype USGN. The perforance of the MLM-PF algorith has been evaluated, and copared with standard PF, in a ground target road networ siulation study. The ground target scenario contains both live recordings fro a USGN and a siulated part. It is shown that the suggested odified MLM-PF algorith in general has an excellent tracing perforance, and outperfors a standard PF which is not taing the additional road inforation into account. ACKNOWLEDGMENT This wor has been partly financially supported by the Swedish and Dutch MOD representatives, as a European MOU, ERG no., research and technology proect. This support is gratefully acnowledged. REFERENCES [] M. Ule and W. Koch,. Road-ap assisted ground oving target tracing, IEEE Transactions on Aerospace and Electronic Systes, vol. 4, pp , October 6. [] D. Salond, M. Clar, R. Vinter, S. Godsill, Ground target odelling, tracing and prediction with road networs, in Proc. of FUSION 7, Conf., Quebec, 7. [3] M. Ean, K. Davstad, and L. Söberg, Ground target tracing using acoustic sensors, in Proc. of IDC 7 Syposiu, Adelaide, 7. [4] M. Ean and E. Sviestins, Multiple Model Algorith Based on Particle Filters for Ground Target tracing, in Proc. Of FUSION 7 Conf., Quebec, 7. [5] H. A. P. Blo and Y. Bar-Shalo, The interacting ultiple odel algorith for systes with Marov switching coefficients, IEEE Trans. on Autoatic Control, Vol. 33, pp , 4. [6] S. Blacan and R. Popoli. Introduction to Net-woring, Design and analysis of odern tracing systes, Artech House, 999. [7] M. Ean. Particle Filters and Data Associations for Multi-target Tracing. In Proc. of FUSION 8 Conf., Cologne, 8. [8] Jaob Stoltz, Kell Davstad, Mattias Böran, David Lindgren, An Unattended and Distributed Ad-Hoc Sensor Networ for Classification and Tracing, Proceedings of CIMI 6 Conf., Enöping, Sweden, 6 [9] H. Johansson, Road-constrained target tracing using particle filter, Master Thesis wor, LITH-ISY-E--8/456 SE, Linöping, 8. [] S. Saha, Y. Boers, H. Driessen, P.K. Mandal, A. Bagchi, Particle based MAP state estiation: A coparison, in Proc. Of FUSION 9 Conf., Seattle,