This paper introduces several advances in methods to evaluate performance of multi-target trackers. First, we modify the classical target and track purity definitions to be more reflective of fragmentation and swap phenomena. Second, we extend performance evaluation to more general track outputs with arbitrary state probability distributions. Finally, we introduce a track-level generalization of the GOSPA metric that offers a scalar performance measure of overall tracking performance.

Practical multi-hypothesis trackers (MHTs) often entail a number of parameters for track confirmation and extraction logic, gating and pruning, most of which are chosen heuristically to tradeoff performance and computational cost. Conceptually, these parameters are unnecessary with optimal MHT processing, as these decisions will fall out from the optimal solution, though perhaps with an increase in processing cost. We demonstrate, however, with a canonical MHT model and its attendant association assignment problem that many of these parameters can be chosen losslessly, that is, they only remove hypotheses that an optimal association solution is guaranteed to remove anyway, and thus strictly improve computational cost, with no loss in tracking performance. At the other end of the spectrum, the tools developed likewise yield a number of relations that detect when parameters are set such that practical tracking is no longer possible.

Multi-sensor multi-target tracking requires the solution to a challenging data association problem. The problem simplifies when a portion of the target state vector and the corresponding sensor data satisfy a particular Markovian assumption. This leads to quantifiable benefits in performance vs. complexity of the tracking solution. This paper summarizes recently-obtained technical advances in graph-based tracking and applies this to a benchmark study with respect to an advanced track-oriented multiple-hypothesis tracking solution.

This paper addresses some concerns that have been expressed in the literature regarding the multiple-hypothesis tracking (MHT) paradigm for multi-target tracking (MTT). We clarify that MHT is a mathematically valid maximum a posteriori (MAP) estimation approach to the MTT problem. We identify some extensions to the MHT approach that have emerged over the years, and discuss the graph-based tracking (GBT) simplification that achieves significant computational reduction by introducing path-independence approximations. We provide some suggestions for future MTT performance evaluation efforts and conclude by indicating some current research directions.

This paper proposes a distributed MHT approach to passive sonar tracking for a field of fixed and moving sensors. It includes single-sensor narrowband and broadband measurement-space tracking at each sensor, followed by 2D Cartesian multi-sensor fusion. Tracking and fusion are performed in real time, with a small delay due to the MHT processing with each component of the architecture. Specific innovations include the use of statistically-consistent measurement-space target statistics, unbiased solution cross-fixing for Cartesian initialization, robust distributed MHT track scoring and management, and temporal uncertainty estimation to support targeting decisions.

Over the past several years, the NATO Undersea Research Centre has conducted extensive research in multisensor networks for undersea surveillance, culminating in the development of the DMHT tracker. In this paper, we discuss upgrades to this technology and its application to maritime surveillance.

This paper provides an overview of the special session on multistatic sonar and radar tracking at FUSION 2006. This includes background on the multistatic tracking working group, a brief description of the datasets and trackers that compose this working group at present, and a detailed discussion of a proposed set of tracker performance metrics. We identify a number of issues associated with performance assessment for target tracking. We conclude with recommendations for continued performance assessment of multistatic trackers

Sonar tracking using measurements from multistatic sensors has shown promise: there are benefits in terms of robustness, complementarity (covariance-ellipse intersection) and of course simply due to the increased probability of detection that naturally accrues from a well-designed data fusion system. It is not always clear what the placement of the sources and receivers that gives the best fused measurement covariance for any target-or at least for any target that is of interest-might be. In this paper, we investigate the problem as one of global optimization, in which the objective is to maximize the information provided to the tracker. We assume that the number of sensors is known, so that the optimization is done in a continuous space. We consider "barrier" scenario and numbers of sensors. The strong variability of target strength as a function of aspect is integral to the cost function we optimize. Doppler information is not discarded when constant frequency (Doppler-sensitive) waveforms are available. Numerical results are given, these suggesting that certain sensor geometries should be used

This paper provides a brief description and performance results for the multistatic sonar trackers developed at NURC. Our analysis is based on common data distributed as part of the multistatic tracking working group (MSTWG), as well as sea trial data. We find that the nearest-neighbor tracker provides reasonable performance in benign environments, while the centralized and distributed MHT trackers have complementary strengths in more challenging scenarios

This paper provides an overview of a multistatic sonar contact-data simulation approach and a dataset generated specifically for tracker algorithm evaluation by the multistatic tracking working group (MSTWG). A brief description of the simulation approach is given, which includes simple sonar equation modeling, resulting in sensor-to-sensor target fading effects, as well as contact localization modeling. We describe the methodology by which a single data set generated using this approach is suitable to evaluate multistatic tracker performance over a range of multi-sensor detection redundancy levels

The MLPDA is based on maximizing statistical likelihood according to a precise model in which there is no process noise. The PMHT (probabilistic multi-hypothesis tracker) provides an alternative perspective: each contact may be taken as independent and a-priori equally-equipped to be target-generated. Our results indicate that the MLPMHT is the better tracker in multi-static data. A further advantage of the MLPMHT is that optimal data association with multiple targets is easily incorporated, whereas in the MLPDA it is approximated by excision of measurements that are "taken" by previously-discovered targets. In this paper we apply the MLPMHT and MLPDAF to several data-sets from the MSTWG (multi-static tracking working group) library: two synthetic and two real ones from NURC, plus one from ARL/UT. We also compare the ML trackers to the IMMPDAFAI, a tracker with no "depth" to its assignments: it is found that the IMMPDAFAI is not able to track effectively in such noisy data. Finally, we report on a new genetic implementation of the MLPMHT