This paper describes the recent development in the random finite set RFS paradigm in multi-target tracking. Over the last decade the Probability Hypothesis Density filter has become synonymous with the RFS approach. As result the PHD filter is often wrongly used as a performance benchmark for the RFS approach. Since there is a suite of RFS-based multi-target tracking algorithms, benchmarking tracking performance of the RFS approach by using the PHD filter, the cheapest of these, is misleading. Such benchmarking should be performed with more sophisticated RFS algorithms. In this paper we outline the high-performance RFS-based multi-target trackers such that the Generalized Labled Multi-Bernoulli filter, and a number of efficient approximations and discuss extensions and applications of these filters. Applications to space situational awareness are discussed.
Multi-target tracking is intrinsically an NP-hard problem and the complexity of multi-target tracking solutions usually do not scale gracefully with problem size. Multi-target tracking for on-line applications involving a large number of targets is extremely challenging. This article demonstrates the capability of the random finite set approach to provide large scale multi-target tracking algorithms. In particular it is shown that an approximate filter known as the labeled multi-Bernoulli filter can simultaneously track one thousand five hundred targets in clutter on a standard laptop computer.
This paper considers the problem of joint multiple target tracking, identification, and classification. Standard
approaches tend to treat the tasks of data association, estimation, track management and classification as
separate problems. This paper outlines how it is possible to formulate a unified a Bayesian recursion for joint
tracking, identification and classification. The formulation is based on the theory of random finite sets or finite set
statistics, and specifically labeled random finite sets, which results in a propagation of a multi-target posterior
which contains not only target information but all available track information. Implementations are briefly
discussed. Where appropriate for particular applications this method can be considered Bayes optimal.
The probability hypothesis density (PHD) filter is a practical alternative to the optimal Bayesian multi-target filter based on
finite set statistics. It propagates only the first order moment
instead of the full multi-target posterior. Recently, a sequential
Monte Carlo (SMC) implementation of PHD filter has been used in
multi-target filtering with promising results. In this paper, we
will compare the performance of the PHD filter with that of the
multiple hypothesis tracking (MHT) that has been widely used in
multi-target filtering over the past decades. The Wasserstein
distance is used as a measure of the multi-target miss distance in
these comparisons. Furthermore, since the PHD filter does not
produce target tracks, for comparison purposes, we investigated
ways of integrating the data-association functionality into the
PHD filter. This has lead us to devise methods for integrating the
PHD filter and the MHT filter for target tracking which exploits
the advantage of both approaches.
In this paper, a solution to the TENET nonlinear filtering challenge is presented. The proposed approach is based on particle filtering techniques. Particle methods have already been used in this context but our method improves over previous work in several ways: better importance sampling distribution, variance reduction through Rao-Blackwellisation etc. We demonstrate the efficiency of our algorithm through simulation.