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.
Modern computational capabilities allow the practical application of Multiple Hypothesis Tracking (MHT) for
difficult tracking conditions. However, even in typical expected scenarios, periods of unusually high target and / or
clutter density may occur that stress the ability of MHT to operate in real-time and under the constraints of limited
computer memory. This paper outlines methods that are being developed to ensure practical application, even though
some performance degradation must be accepted, during these difficult conditions. These methods include the adaptive
choice of track and hypothesis pruning parameters, IMM filtering models and new track initiation strategies as a function
of the latency between the time that current observations are received and the track processing time. Methods to ensure
that memory constraints are satisfied are also discussed. The methods are illustrated with examples from simulated
missile defense scenarios where periods of very high target density are expected and a ground target tracking scenario
with real radar data.
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.
Preliminary tracking system design and analysis is typically done using simulated data in which the target truth is known and many techniques have been developed for evaluating performance under these conditions. However, there is a notable lack of any consistent approach for evaluating tracker performance for real data in which there may be an unknown number of targets of opportunity whose trajectories are not known. In addition, the background clutter/false alarm environment may be unknown so that an important analysis task is to determine the most accurate background models. This paper proposes a set of criteria for evaluating the tracks that are formed using real data collected in the field in the presence of an unknown number of targets of opportunity. These criteria include duration, update history, and measures of kinematic and data association consistency. A scoring method is developed and the use of these criteria for system design is discussed.
Since its initial definition, about 25 years ago, the potential data association performance enhancements associated with Multiple Hypothesis Tracking (MHT) have been widely accepted. However, the actual practical implementation of MHT has been impeded by the perception that its complexity precludes real-time application. The purpose of this paper is to show that modern computational capabilities and newly developed MHT algorithm efficiencies make real-time MHT implementation feasible even for scenarios with large numbers of closely spaced targets.
The paper begins by outlining the elements of our MHT algorithm and by defining a typical stressing scenario, with about 100 closely spaced targets, which is used for evaluation of real-time MHT implementation capability. It then presents the processing times required for each of the MHT algorithm elements on a 866Mhz Pentium III computer. Finally, it also presents the memory requirements. Conclusions are that real-time implementation is currently feasible for typical stressing scenarios using the 866Mhz Pentium III computer or other similar modern machines. The extension to larger scenarios with future computer systems is outlined.
Proc. SPIE. 4473, Signal and Data Processing of Small Targets 2001
KEYWORDS: Target detection, Infrared search and track, Signal to noise ratio, Detection and tracking algorithms, Computing systems, Computer simulations, Electroluminescence, Data processing, Signal processing, Palladium
Hypothesis formation is a major computational burden for any multiple hypotheses tracking (MHT) method. In particular, a track-oriented MHT method defines compatible tracks to be tracks not sharing common observations and then re-forms hypotheses from compatible tracks after each new scan of data is received. The Cheap Joint Probabilistic Data Association (CJPDA) method provides an efficient means for computing approximate hypothesis probabilities. This paper presents a method of extending CJPDA calculations in order to eliminate low probability track branches in a track-oriented MHT method. The method is tested using IRST data. This approach reduces the number of tracks in a cluster and the resultant computations required for hypothesis formation. It is also suggested that the use of CJPDA methods can reduce assignment matrix sizes and resultant computations for the hypothesis-oriented (Reid’s algorithm) MHT implementation.
This paper considers the problem of tracking dim unresolved ground targets and helicopters in heavy clutter with a ground based sensor. To detect dim targets the threshold must be set low which result in a large number of false alarms. The tracker typically uses the target dynamics to prevent the false tracks. The interesting aspect of this problem is that the targets may be or may become stationary. The tracks of stationary targets are difficult to discriminate from tracks formed by persistent false alarms.
The benchmark problem addresses the efficient allocation of an agile beam radar in the presence of highly maneuverable targets and radar ECM. The multisensor benchmark tracking solution s aided by the presence of a scanning IRST. This paper presents methods for applying an IMM/MHT tracker to this multiple sensor tracking and resource allocation problem. The paper discusses the manner is which IMM/MHT tracking and data association methods lead to efficient agile beam radar allocation and presents results showing that this approach is significantly more efficient than previously proposed methods when only radar data are used. It presents a hybrid multisensor tracking architecture in which an IMM/MHT tracker operating on IRST data provides the global IMM/MHT tracker with selected observations. Simulation results quantify the potential improvement from the use of advanced tracking methods and IRST data to enhance agile beam radar tracker capability.
Interacting multiple model (IMM) filtering and multiple hypothesis tracking (MHT) represent the most accurate methods currently available for tracking multiple maneuvering targets in cluttered environments. Although these methods are complex, modern computational capabilities make their combined implementation feasible for modern tracking systems. This paper discusses alternative approaches for developing a combined IMM/MHT tracking system and describes specific implementations that have been developed for radar and IRST applications. Simulation results for both radar and IRST systems are presented. Track maintenance and accuracy performance for the IMM/MHT system is compared with that obtained from an MHT tracker using a conventional filtering approach. Results indicate that the improvements in data association derived from the use of IMM filtering with MHT may be comparable to the well known IMM improvements in track accuracy. The addition of multiple filter models to MHT data association has the potential to significantly increase computational requirements. Thus, several compromises, described in this paper, have been developed in order to assure computational feasibility. Preliminary estimates of computational requirements are given in order to demonstrate implementation feasibility.
Proc. SPIE. 2759, Signal and Data Processing of Small Targets 1996
KEYWORDS: Target detection, Infrared search and track, Signal to noise ratio, Detection and tracking algorithms, Computing systems, Monte Carlo methods, Missiles, Electronic filtering, Algorithm development, Filtering (signal processing)
This paper describes the use of multiple hypothesis tracking (MHT) for the IRST Shipboard Self Defense application. This application features a highly variable clutter background, such as produced by sun glint, and maneuvering targets. The paper presents a technique for including features, such as measured SNR, in the track score. Performance results are presented for the case of a simulated missile target inserted in an ocean background. The paper presents computer timing and sizing results to show that recently developed algorithm efficiencies and computational capabilities make real-time MHT tracker operation feasible within the near future. A comparative study of track maintenance shows the significant potential performance improvement of MHT when compared with other data association methods.
Long range detection and tracking of moving targets against clutter requires advanced signal and track processing techniques in order to exploit the ultimate capabilities of modern electro-optical sensors. These include three- dimensional filtering and multiple hypothesis tracking. Unfortunately, features present in real backgrounds can lead to false alarms which must be recognized in order to achieve a low false track rate. This paper describes one approach which was successful at mitigating clutter-induced false tracks while maintaining the low thresholds necessary for the detection of weak targets. This technique uses information derived in the signal processor describing the local background as additional discriminants in the track processor to identify false tracks caused by clutter leakage. We present an overview of the 3D signal track/processor, the false track mitigation methodology, and experimental results against real background data.
A multiple hypothesis tracking (MHT) system produces multiple data association hypotheses that potentially consist of several tracks on the same target. The relative likelihood of these tracks changes as data are received. Also, the manner in which an MHT system produces and deletes tracks leads to a potential discontinuity in the track numbers associated with a given target. Thus, the direct output of an MHT tracker may be difficult to interpret on a system display or to use to perform track-to-track association in a multiple sensor tracking system. This paper presents a methodology so that the best tracks on a set of targets can be identified and a link provided with previously identified tracks. This allows a multiple sensor tracking system to maintain track-to-track association histories over time. Also, although track numbers change internal to the MHT logic, the methods presented in this paper lead to a user output such that a single track number is maintained on a given target throughout an encounter. Thus, this paper presents a solution to one of the major practical problems associated with the implementation of an MHT system.
Proc. SPIE. 1698, Signal and Data Processing of Small Targets 1992
KEYWORDS: Target detection, Infrared search and track, Signal to noise ratio, Infrared sensors, Infrared imaging, Detection and tracking algorithms, Surveillance systems, Monte Carlo methods, Data processing, Signal processing
This paper describes an overall methodology for the application of a multiple hypothesis tracking (MHT) algorithm to the IR surveillance system problem of tracking dim targets in a heavy clutter or false alarm background. First, It discusses the manner in which the detection and tracking systems are jointly designed to optimize performance. Next, it presents approximate methods that can conveniently be used for preliminary system design and performance prediction. Finally, it discusses the use of a detailed Monte Carlo simulation for final system evaluation and presents results illustrating the proposed methods and comparing predicted and simulation performance.