This paper discusses the application of multiple hypothesis tracking (MHT) to the processing of
ground target data collected with a long range surveillance radar. A key element in the successful tracking
of ground targets is the use of road networks. Thus, the paper begins with an overview of the alternative
approaches that have been considered for incorporating road data into a ground target tracker and then it
gives a detailed description of the methods that have been chosen. The major design issues to be addressed
include the manner in which road filter models are included into a Variable-Structure Interacting Multiple
Model (IMM) filtering scheme, how the road filter models are chosen to handle winding roads and
intersections, and the tracking of targets that go on and off-road.
Performance will be illustrated using simulated data and real data collected from a large surveillance
area with a GMTI radar. The area considered contains regions of heavy to moderate target densities and
clutter. Since the real data included only targets of opportunity (TOO), it was necessary to define metrics to
evaluate relative performance as alternative tracking methods/parameters are considered. These metrics are
discussed and comparative results are presented.
Particle filter tracking, a type of sequential Monte Carlo method, has long been considered to be a
very promising but time-consuming tracking technique. Methods have been developed to include a particle
filter as part of a Variable Structure, Interactive Multiple Model (VS-IMM) structure and to integrate it into
the Multiple Hypothesis Tracker (MHT) scoring structure. By integrating a particle filter as just one of
many filters in Raytheon's MHT, the particle filter is applied sparingly on difficult off-road targets. This
dramatically reduces the computation time as well as improves tracking performance in circumstances in
which the other filters do not excel. Moreover, terrain information may be taken into account in the
particle propagation process. In particular, an Unscented Particle Filter (UPF) was implemented in order
to address the potential dominance of a small set of degenerate particles and/or poor prior distribution
sampling from hampering the ability of the particle filter to accurately handle a maneuver.
The Unscented Particle Filter treats every particle as its own Kalman filter. After the distribution
of particles is adjusted in order to take into account the terrain, each particle is divided into sigma point
states. These sigma points are propagated forward in time and then recombined to form a new composite
particle state and covariance. These reformed particles are used in scoring and can be updated with a new
observation. Since the Unscented Particle Filter includes the covariances in these calculations, this particle
filter approach is more accurate and potentially requires fewer particles than an ordinary particle filter. By
adding an Unscented Particle Filter to the other filters in an MHT tracker, the advantages of the UPF can be
utilized in an efficient manner in order to enhance tracking performance.
It is widely accepted that Classification Aided Tracking (CAT) has the potential to maintain continuous tracks on important targets. Moreover, when augmented with target behavior, a joint tracking and ID system can enhance the data association process for ground tracking systems. It is also recognized that it is likely that some targets in any scenario may not be included in a database, and the presence of such confusers would diminish both tracking and ID performance. Moreover, even with ID information, tracks may switch targets. Thus, a joint tracking and identification architecture has been developed which addresses the issues of both confusers and track ID switching. These methods are being tested using simulated dynamic ground targets and radar High Range Resolution (HRR) data provided by the Moving and Stationary Target Acquisition and Recognition (MSTAR) project.
The paper begins by giving an overview of the IMM/MHT tracker that has been designed to handle the unique characteristics (such as on-off road behavior) of the ground target tracking problem. Then, a joint tracking identification methodology is described. Implementing this approach, target behavior (such as being part of a group, speed, and on/off road motion) can be used both in the data association and for target type information. A Dempster-Shafer method is used for combining all classification-related data. In addition, confusers are taken into account by incorporating the information from targets that are in the database. The track score, required in all MHT data association decisions, is augmented with a feature-related term derived from the conflict term computed from an application of Dempster's Rule. The histories of the most likely ID for each track are checked to identify possible switches, and if tracks are believed to have switched IDs, then the state and the covariances of these tracks are exchanged so that future observations may be consistent with the original targets. Finally, the paper illustrates the proposed methods using results from a detailed simulation of target convoys, with and without confuser targets, that perform on and off road maneuvers. Results using MSTAR HRR data are presented for Classification-Aided (CAT) approaches to feature-aided tracking.
It is widely accepted that feature data will be necessary in order to aid the data association for ground target tracking systems that are required to maintain continuous tracks on important targets. It is also recognized that target behavior as determined by a tracking system can aid target type identification. Thus, noting that the tracking and ID functions are complementary, a joint tracking and identification architecture has been developed. These methods are being tested using simulated dynamic ground targets and radar High Range Resolution (HRR) data provided by the Moving and Stationary Target Acquisition and Recognition (MSTAR) project. The paper begins by giving an overview of the IMM/MHT tracker that has been designed to handle the unique characteristics (such as on-off road behavior) of the ground target tracking problem. Then, a joint tracking identification methodology is described. Implementing this approach, target behavior (such as being part of a group, speed, and on/off road motion) can be used both in the data association and for target type information. A Dempster-Shafer method is used for combining all classification-related data. The track score, incorporated in all MHT data association decisions, is augmented with a feature-related term derived from the conflict term computed from an application of Dempster's Rule. Finally, the paper illustrates the proposed methods using results from a detailed simulation of target convoys that perform on and off road maneuvers. Results using MSTAR HRR data are presented for both Signature-Aided (SAT) and Classification-Aided (CAT) approaches to feature-aided tracking.
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