Darin T. Dunham

Multiple target tracking from images using the maximum likelihood HPMHT

In this paper, we address the problem of passive tracking of multiple targets with the help of images obtained from passive infrared (IR) platforms. Conventional approaches to this problem, which involve thresholding, measurement detection, data association and filtering, encounter problems due to target energy being spread across multiple cells of the IR imagery. A histogram based probabilistic multi-hypothesis tracking (H-PMHT) approach provides an automatic means of modeling targets that are spread in multiple cells in the imaging sensor(s) by relaxing the need for hard decisions on measurement detection and data association. Further, we generalize the conventional HPMHT by adding an extra layer of EM iteration that yields the maximum likelihood (ML) estimate of the number of targets. With the help of simulated focal plane array (FPA) images, we demonstrate the applicability of MLHPMHT for enumerating and tracking multiple targets.

Multi-frame assignment PMHT that accounts for missed detections

Probabilistic multi-hypothesis tracking (PMHT) is an algorithm for tracking multiple targets when measurement-to- target assignments are unknown and must be jointly estimated with the target tracks. Multi-frame assignment PMHT (MF- PMHT) is an algorithm designed to mitigate some performance problems associated with PMHT. In MF-PMHT, the PMHT algorithm is applied to multi-frame sequences in the last L frames of data and considers the set of all possible measurement sequences. While effective in improving tracking performance compared to PMHT, performance of the original MF-PMHT degrades when the target single-frame detection probability is non-unity. This is because missed detections are not considered in the multi-frame sequences. A new MF-PMHT implementation is derived in this paper which explicitly considers missed detections in the multi-frame sequences. Performance of this MF-PMHT is compared to the original MF-PMHT algorithm as well as to a Homothetic PMHT. Simulation results indicate that the new MF- PMHT algorithm performs the same as the original algorithm when there are no missed detections and also performs better than the alternative algorithms considered when there are missed detections.

Tracking algorithm speed comparisons between MHT and PMHT

The Probabilistic Multi-Hypothesis Tracker (PMHT) is an emerging tracking algorithm that appears to have the potential to compete with other well-established tracking algorithms. One of the values that the PMHT brings to the tracking problem is its computational efficiency that grows linearly as the number of targets increases, whereas most tracking algorithms increase exponentially as targets increase. Knowing this, how much does this computational efficiency for the PMHT translate into an algorithm speed advantage? The Multi-Hypothesis Tracker (MHT) was first presented in the late 1970s. Since then significant work has been done in order to improve this robust algorithm, and today the MHT is one of the leading tracking algorithms. Taking an efficient coding of the MHT, it is used as a comparison for the PMHT in terms of algorithm speed. In order to make this comparison objectively, the PMHT is run against the MHT in a common environment. Results have been produced for both the single target scenario and the multiple target scenarios.

Advancement of an algorithm

The Probabilistic Multi-Hypothesis Tracker (PMHT) was developed in the early 1990s by Roy Streit and Tod Luginbuhl. Since that time many advances and improvements have been made to this elegant algorithm that is linearly efficient in processing as the number of targets, sensors, and clutter increases. This paper documents the many advances to the PMHT by several different contributors over the past two decades. The history continues and looks as promising as ever for this algorithm as we present the latest advancement—the Maximum Likelihood, Histogram Probabilistic Multi-Hypothesis Tracker (ML-HPMHT)—and the exciting results of this potential game-changer in tracking unresolved, dim targets in highly cluttered environments. This new algorithm, which we are calling the Quanta Tracking algorithm, detects and tracks with high accuracy targets that are unresolved in pixels or range bins.

Solving the distributed multiple platform tracking problem with the PMHT algorithm

In 1995, Streit and Luginbuhl introduced a new tracking algorithm1, which offered a balance between the single-frame approach of the Probabilistic Data Association Filter (PDAF) and the multiple frame approach of the Multiple Hypothesis Tracker (MHT). With single-frame tracking algorithms, only information that has been received to date is used to determine the association between tracks and measurements. These decisions are made based on available data and are not changed even when future data may indicate that a decision was incorrect. On the other hand, in multi-frame algorithms, hard decisions are delayed until some time in the future, thus allowing the possibility that incorrect association decisions may be corrected with more data. This paper presents the initial results of some new research using the PMHT algorithm as a composite tracker on distributed platforms. In addition, the methods necessary to implement the PMHT in a realistic simulation are discussed. It further describes the techniques that have been tried to ensure a single integrated air picture (SIAP) across the platforms. The PMHT uses both past and present data without enumerating most of the possibility measurement-to-track assignments. Instead the PMHT uses probabilistic weightings via Gaussian mixtures to define the relationship between measurements and tracks.

Multiple hypothesis tracking for a distributed multiple platform system

Methods have been developed to apply Multiple Hypothesis Tracking (MHT) to a distributed multiple platform system for tracking missile targets. The major issue that must be addressed is the requirement for a single integrated air picture (SIAP) to be maintained across the multiple platforms. Communication delays and failures mean that the platforms will, in general, form different MHT hypotheses with resultant different output tracks presented to the users. Thus, logic, described in this paper, has been developed to ensure that similar data association decisions will be made across the multiple platforms.

Quanta tracking algorithm for multiple targets with velocity

The Quanta Tracking (QT) algorithm is a fairly new algorithm that is showing very promising results tracking unresolved, dim targets in highly cluttered environments. Traditional detection and tracking approaches use thresholding and signal processing to declare measurements that are then fed into the tracker. The QT algorithm does this all organically in an optimal manner, called “track-before-detect”. The algorithm requires no thresholding of the data such that all of the data is utilized. In the latest paper on this algorithm, we wrote about accounting for stationary targets and not tracking them. In this paper, we outlined two approaches for not tracking stationary targets. The first approach, implemented previously, removed any tracks that were below a certain velocity threshold. This allowed the algorithm to process the energy from these “stationary” targets, but then removed them from the set of tracks reported. Now we have implemented a second approach that prevents these “stationary” tracks from forming during the processing of the data. This approach has an advantage in that the algorithm will not spend any resources on these undesired stationary targets. In this paper, we show results from this latest approach as well as compare these results against the previous approach for not tracking stationary targets.

Air autonomous vehicle benchmark

The term benchmark originates from the chiseled horizontal marks that surveyors made, into which an angle-iron could be placed to bracket (“bench”) a leveling rod, thus ensuring that the leveling rod can be repositioned in exactly the same place in the future. A benchmark in computer terms is the result of running a computer program, or a set of programs, in order to assess the relative performance of an object by running a number of standard tests and trials against it. The Benchmark simulation environments began in the 1990s and continue today with many different variants including the Ballistic Missile Defense (BMD), Integrated Air/Missile Defense (IAMD), Chemical Biological Defense (CBD), Multiple-Input, Multiple-Output (MIMO) Radar, Electronic Countermeasures (ECM), and Electronic Attack (EA) Benchmarks. This latest variant of a benchmark, Air Autonomous Vehicle (AAV), for target tracking applications models autonomous air vehicles flying over littoral regions tracking surface vessels. This paper covers the capabilities of the AAV Benchmark, the initial scenario that was developed, and results showing the tradeoff between two centralized tracking algorithms.

PMHT for fused tracking

Fusing data together for target tracking is a complex problem. There are two key steps. First, the raw observations must be associated with existing tracks or used to form new tracks. Once the association has been done, then the tracks can be updated and filtered with the new data. The updating and filtering is usually the easier of the two parts and it is the association that can lead to most of the complexity in target tracking. When associating data (either measurements or tracks or both) with existing tracks, the separation between the tracks is critical to how difficult the association decisions will be. If the tracks are widely separated then the association decisions can be relatively easy. On the other hand, when the tracks are closely spaced the association decisions can be very difficult or nearly impossible. When the tracks or measurements are in three dimensions (such as with active sensors) the association can be accomplished in all three dimension thus making an easier distinction of targets that may be very close in two dimensions, but distant in the third dimension. However, when there are only two dimensions (as for passive sensors) observed by a sensor, targets that are widely separated may appear to be very close or even unresolved. In this paper, we will discuss the issues involved with applying the Probabilistic Multi-Hypothesis Tracking (PMHT) algorithm to fusing either measurements or tracks from passive sensors.

Simulation assessment of RCS-aided multiple target tracking

Closely-spaced (but resolved) targets pose a significant challenge for single-frame unique measurement-to-track data association algorithms. This is due to the similarity of the Mahalanobis distances between the closely-spaced measurements and tracks. Contrary to conventional wisdom, adding target feature information (e.g., target amplitude) does not necessarily improve the probability of correctly assigning measurements to tracks. In this paper, the theoretical limitations of using radar cross section (RCS) data to aid in measurement-totrack association are reviewed. The results of a high-fidelity simulation assessment of the benefits of RCSaided measurement-to-track association (using the Signal-to-Noise Ratio) are given and other possibilities for RCS-aided tracking are discussed. Namely, we show the analytical results of our investigation into using RCS information to determine the presence of merged measurements.