This work addresses the problem of tracking multiple moving targets by recursively estimating the joint multitarget probability density (JMPD). Estimation of the JMPD is done in a Bayesian framework and provides a method for tracking multiple targets which allows nonlinear target motion and measurement to state coupling as well as nonGaussian target state densities. The JMPD technique simultaneously estimates both the target states and the number of targets in the surveillance region based on the set of measurements made. We give an implementation of the JMPD method based on particle filtering techniques and provide an adaptive sampling scheme which explicitly models the multitarget nature of the problem. We show that this implementation of the JMPD technique provides a natural way to track a collection of targets, is computationally tractable, and performs well under difficult conditions such as target crossing, convoy movement, and low measurement signal-to-noise ratio (SNR).

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Multitarget tracking using the joint multitarget probability density

Multi-armed bandit (MAB) problems are a class of sequential resource allocation problems concerned with allocating one or more resources among several alternative (competing) projects. Such problems are paradigms of a fundamental conflict between making decisions (allocating resources) that yield high current rewards, versus making decisions that sacrifice current gains with the prospect of better future rewards. The MAB formulation models resource allocation problems arising in several technological and scientific disciplines such as sensor management, manufacturing systems, economics, queueing and communication networks, clinical trials, control theory, search theory, etc. (see [88] and references therein).

Multi-Armed Bandit Problems

This paper considers the problem of simultaneously detecting and tracking multiple targets. The problem can be formulated in a Bayesian framework and solved, in principle, by computation of the joint multitarget probability density (JMPD). In practice, exact computation of the JMPD is impossible, and the predominant challenge is to arrive at a computationally tractable approximation. A particle filtering scheme is developed for this purpose in which each particle is a hypothesis on the number of targets present and the states of those targets. The importance density for the particle filter is designed in such a way that the measurements can guide sampling of both the target number and the target states. Simulation results, with measurements generated from real target trajectories, demonstrate the ability of the proposed procedure to simultaneously detect and track ten targets with a reasonable sample size

/pdf/a-bayesian-approach-to-multiple-target-detection-and-5cv9tn7ejl.pdf

A Bayesian Approach to Multiple Target Detection and Tracking

In recent years an abundance of technical papers and books have been written on several topics of radar signal processing. The detection of radar targets against ground and sea clutter is a problem of great interest in the radar community. The fusion of signals from different radar to improve detection performance is another relevant topic of research and application. These are two of several topics that have been extensively described in the technical literature. Needless to say that the consultation of technical references plays a key role in the daily work of researchers and engineers involved in the radar field. We collected almost 700 references in a single document to facilitate our work and the work of other colleagues of the radar community. The collection of references is by no means exhaustive; the period of screening mainly covers the last two decades.

Selected list of references on radar signal processing

Asynchronous particle filter for tracking using non-synchronous sensor networks

We evaluate the performances of several distributed constant false-alarm rate (CFAR) tests operating in different background clutter conditions. The analysis considers the detection of Rayleigh target in various clutters with the possibility of differing clutter power levels in the test cells of distributed radars. Numerical results studied for a two-radar system show how the false-alarm rate of the maximum order statistic (MOS) test changes with differences in the clutter power levels of the test cells. The analysis for the detection of Rayleigh target in Rayleigh clutter indicates that, with the power levels of differing test cells, the OR fusion rule can be quite competitive with the new normalized test statistic (NTS). However, for the detection of Rayleigh target in Weibull or K-distributed clutter, the results show that NTS outperforms both the OR and the AND rules under the condition of large signal-to-clutter power ratio and moderate shape parameter values.

/pdf/performance-of-distributed-cfar-test-under-various-clutter-1o5agje412.pdf

C.H. Gowda

Papers

Performance of distributed CFAR test under various clutter amplitudes