Factors affecting the performance of an algorithm for tracking multiple targets observed using a pixelized sensor are studied. A pixelized sensor divides the surveillance region into a grid of cells with targets generating returns on the grid according to some known probabilistic model. In previous work an efficient particle filtering algorithm was developed for multiple target tracking using such a sensor. This algorithm is the focus of the study. The performance of the algorithm is affected by several considerations. The pixelized sensor model can be used with either thresholded or non-thresholded measurements. While it is known that information is lost when measurements are thresholded, quantitative results have not been established. The development of a tractable algorithm requires that closely-spaced targets are processed jointly while targets which are far apart are processed separately. Selection of the clustering distance involves a trade-off between performance and computational expense. A final issue concerns the computation of the proposal density used in the particle filter. Variations in a certain parameter enable a trade-off between performance and computational expense. The various issues are studied using a mixture of theoretical results and Monte Carlo simulations.
The problem of tracking multiple maneuvering targets is considered. The usual multiple model approach is adopted in which maneuvering target motion is modeled by assuming that the target motion at each point in time can be described by one of a finite set of dynamic models. Transitions between each mode of target motion are assumed to be Markovian. Target positions are measured in polar coordinates leading to a nonlinear measurement equation. A particle filter is proposed as a solution to the problem. The proposed algorithm seeks to improve upon the performance of a previously proposed particle filter by using measurement-directed proposals and exploiting the structure of the measurement likelihood. The performance analysis focuses on targets which perform coordinated turn maneuvers. An improved model for target motion in this regime is suggested. The performance analysis, using Monte Carlo simulations, demonstrates the improved performance of the proposed algorithm compared to the previously proposed particle filter and the standard Gaussian approximation, the IMM-JPDAF.
Target tracking algorithms have to operate in an environment of uncertain measurement origin, due to the presence of randomly detected target measurements as well as clutter measurements from unwanted random scatterers. A majority of Bayesian multi-target tracking algorithms suffer from computational complexity which is exponential in the number of tracks and the number of shared measurements. The Linear Multi-target (LM) tracking procedure is a Bayesian multi-target tracking approximation with complexity which is linear in the number of tracks and the number of shared measurements. It also has a much simpler structure than the "optimal" Bayesian multi-target tracking, with apparently negligible decrease in performance. A vast majority of target tracking algorithms have been developed with the assumption of infinite sensor resolution, where a measurement can have only one source. This assumption is not valid for real sensors, such as radars. This paper presents a multi-target tracking algorithm which removes this restriction. The procedure utilizes a simple structure of LM tracking procedure to obtain a LM Finite Resolution (LMfr) tracking procedure which is much simpler than the previously published efforts. Instead of calculating the probability of measurement merging for each combination of potentially merging targets, we evaluate only one merging hypotheses for each measurement and each track. A simulation study is presented which compares LMfr-IPDA with LM-IPDA and IPDA target tracking in a cluttered environment utilizing a finite resolution sensor with five crossing targets. The study concentrates on the false track discrimination performance and the track retention capabilities.
In a Bayesian framework, all single target tracking problems reduce to recursive computation of the posterior density of the target state. Particle filters approximate the optimal Bayesian recursion by propagating a set of random samples with associated weights. In the last decade, there have been numerous contributions to the theory and applications of particle filters. Much study has focussed on design issues such as appropriate selection of the importance density, the use of resampling techniques which mitigate sample degeneracy and the choice of a suitable random variable space upon which to implement the particle filter in order to minimise numerical complexity. Although the effect of these design choices is, in general, well known, their relevance to target tracking problems has not been fully established. These design issues are considered for single target tracking applications involving target manoeuvres and clutter. Two choices of importance density are studied and methods for enhancing particle diversity through the avoidance of particle duplication in the resampling step are considered for each importance density. The possibility of reducing the dimension of the space over which the particle filter is implemented is considered. Based on simulation results, a few key observations are drawn about which aspects of particle filter design most influence their performance in target tracking applications. The numerical simulations also provide insights into the relationship between the state dimension and the number of particles needed to improve upon the performance of the standard tracking filters.