An Efficient Data-Driven Particle PHD Filter for Multitarget Tracking

In this paper, we propose an efficient data-driven particle probability hypothesis density (PHD) filter for real-time multitarget tracking of nonlinear/non-Gaussian system in dense clutter environment. In specific, the input measurements are first classified into two sets, namely survival measurements and spontaneous birth measurements, after eliminating clutters by using existing historic state data of targets. Since most clutters do not participate in the complex weight computation of particle PHD filter, better real-time performance can be achieved. The tracking performance is also improved because the survival measurements are used for survival targets and the spontaneous birth measurements are used for spontaneous birth targets, resulting in less interference from each other and from clutters. Extensive simulations validate the improvement of both the real-time performance and tracking performance of the proposed data-driven particle PHD filter in comparison with the traditional particle PHD filter.