Multitarget tracking using the joint multitarget probability density

TL;DR: This work addresses the problem of tracking multiple moving targets by recursively estimating the joint multitarget probability density (JMPD) and gives an implementation of the JMPD method based on particle filtering techniques and provides an adaptive sampling scheme which explicitly models the multitarget nature of the problem.

Abstract: 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).