GRoup IMM Tracking utilizing Track and Identification Fusion

The GRoup IMM Tracking (GRIT) algorithm combines the Joint Belief-Probability Data Association (JBPDA) approach with a Variable Structure Interactive Multiple Model (VS-IMM) estimator to track, identify, and group multiple moving targets. Using an application of the VS-IMM, developed by Bar Shalom and Li, a Multiple Validation Gate Model (MVGM) is introduced that captures the behavior of highly maneuvering targets through move-stop-move cycles. Incorporated within the GRIT model are Targets Constrained on Road Networks (TCORN), Data Dropout, and High Maneuver Terrain (HMT) features. These features are assessed in a group tracking scenario to demonstrate how multiple targets can be represented as groups to reduce the amount of tracked information presented on a display. One of the benefits of the GRIT model is the ability to track and identify Moving and Stopping Targets (MST) for groups of targets that are undergoing merging and splitting behavior. Preliminary results are presented to demonstrate the incorporation of the MVGM, TCORN, HMT, and MST features to facilitate group tracking. 1.0 INTRODUCTION The evolution of target tracking has resulted in many algorithms suited for various multitarget tracking applications [1-7]. In advancing the tracking field, one issue that plagues the implementation of multitarget tracking applications is that presenting a large number of targets on a display exceeds the human’s ability to follow the tracks [11]. Humans naturally group targets to a manageable set for effective comprehension. In order to facilitate the implementation of tracking algorithms for the user [9-11], we seek to group the targets before presenting the track solution to a display to minimize clutter and enhance situational awareness. The JPDAF algorithm [5] effectively tracks multiple targets and improvements were made in the JBPDAF algorithm [9,10] to add identification (ID) information [9] to eliminate clutter and estimate the target number. Fusion of multisensor information [13,14] can improve track estimates. Additionally, using multiple sensors for tracking, detection [15], and identification [11] can significantly improve track estimates. Examples of track and ID fusion, such as High Range Resolution radar (HRR) [27, 28] show improved tracking results [21, 22]. One of the limitations of the JBPDAF algorithm was capturing moving targets with sharp turns. In an effort to capitalize on detecting moving targets that stop, turn, and move again; it was determined that the IMM algorithm could be useful in tracking high maneuvering targets [25]. The choice of incorporating the IMM with the JBPDAF results from the analysis of the IMMPDAF [18, 20] developments and the benchmark problem [8] where the IMM outperformed the Kalman Filter with results comparable to the MHT algorithm [32]. The development of the VSIMM [3, 23, 24] advances a multiple model estimator (MME) [26] since all models need not run at all times. Since the MHT algorithm in a move-stop-move scenario is costly [12], we seek to develop a feasible group tracking algorithm for move-stop-move targets based on the JBPDAF [9,10] and the VS-IMM [3, 17]. Group tracking can be performed with measurement information alone; however, by utilizing sensor fusion applications, additional information from other sensors might provide information to track and identify the targets. Blasch [10] presented a group tracking algorithm which utilized track and ID information; however the analysis of the group ID was made for similar type targets. No affiliation of target types was known since the 1 E.P.B. Email [erik.Blasch@wpafb.af. mil] T. Connare and E. Blasch “Group IMM tracking utilizing Track and Identification Fusion,” Proceedings of the Workshop on Estimation, Tracking, and Fusion; A Tribute to Yaakov Bar Shalom, Monterey, CA, May 2001, 205 -220. 2 combination of group information was based on similar targets. This paper includes the affiliation of target types using decision logic from sensor fusion to infer the group structure. Since certain targets with the same allegiance might be closely spaced, additional information is needed to determine how to combine targets for group behavior. Section 2 overviews the GRIT group tracking methodology which utilizes Bar Shalom’s VS-IMM approach [3]. Section 3 develops a multiple model of validation gates, based on VS-IMM, to improve the track of highly maneuvering targets. Section 4 presents criteria for targets constrained on roads [3] and Section 5 presents a novel approach for tracking moving and stationary targets. Section 6 applies these methods in the GRIT algorithm to facilitate the track of obscured targets and Section 7 draws conclusions of the work. 2.0 GROUP TRACKING Current research has initiated the topic of group tracking [10, 33, 35, 36] which is a request of field operations users. For the group tracking effort, the state and measurement equations for all targets are: x̂(k + 1 | k) = F(k) x̂(k | k) (1) . ẑ(k + 1 | k) = H( k + 1) x̂(k + 1 | k) (2) The covariance of the predicted state is, P(k + 1| k) = F (k) P(k | k) FT(k) + Q(k) (3) and the covariance associated with the updated state is P(k | k) =β0(k) P(k | k 1) + [1 β0(k)] P c(k | k) P ~ (k) (4) All measurements received from the moving target indicator (MTI) at time k are then compared to the predicted measurements to ascertain whether or not the measurements fall within the kinematic gate. The validation region is the elliptical region V(k, γ) = {z : [z ẑ(k | k 1 )]T S-1(k) [z ẑ(k | k -1)] ≤ γ } (5) where γ is the gate threshold. These equations introduce the basics for the functionality of the JBPDAF and further information can be gathered from [3, 5] and [9] to update these equations for the group scenario [11]. Adapting the JBPDA filter to accommodate group tracking essentially requires performing the logic of the JPDA [5] twice once for the performance of measurement-to-track association and once for the performance of trackto-group association. Both association filters are similar to each other with the following exceptions. For measurement-to-track association, the inputs to the JBPDA are the measurements from the MTI and the highrange resolution radar (HRR) and the outputs are the tracks. For track-to-group association, the inputs are the JBPDA track and IDs and the output are the group estimates. Another difference is that for track-to-group association, there are more joint association events as a result of having two or more tracks capable of belonging to a single group. For measurement-to-track association, only one measurement could be assigned per track which is based on a mutual exclusivity. But this is no longer the case for track-to-group association since more than one track can be assigned to a group. The JBPDAF simultaneous track and ID algorithm incorporates detection (from the MTI), recognition and ID (from the HRR), and tracking (utilizing a modified JPDAF) [9]. One of the weaknesses of the approach is that the ID is dependent on the prediction of the tracking algorithm to assess the orientation (or target articulation) of the target for ID. Blasch and Hong [11] utilized the fusion of the track and ID articulation values to update the T. Connare and E. Blasch “Group IMM tracking utilizing Track and Identification Fusion,” Proceedings of the Workshop on Estimation, Tracking, and Fusion; A Tribute to Yaakov Bar Shalom, Monterey, CA, May 2001, 205 -220. 3 resulting fused articulation angle. However, it is surmised that a better way to capture a target’s behavior is to use the IMM algorithm [3]. Thus, the task at hand is to couple the JBPDAF with the IMM to capture group behavior as shown in Figure 1. The use of IMM will allow the tracker to better capture the track of highly maneuvering targets for an assessment of the articulation of the group of targets. The mathematics for both the IMM [5] and the JBDAF [9] algorithms will remain the same with adaptations to incorporate group tracking and ID. Some differences result from the interaction of the JBPDA with the IMM. The IMM is a high-level algorithm which sets up and calls the JBPDA. The IMM can call multiple JBPDA models into effect and uses an optimal combination of these models to determine the final state and covariance estimates. The IMM produces a mixed initial condition consisting of state estimate and covariance information that is input to the JBPDA filter. The output of the JBPDA, is the state estimate and covariance information to be used by the IMM for a determination of an optimal combination. The JBPDA also outputs to the IMM the innovation and measurement covariance information necessary for the IMM to determine the likelihood functions for each JBPDA model. Figure 2 and Figure 3 shows the advantages of coupling IMM with JBPDA. 150