Modified Gaussian Mixture Probability Hypothesis Density Filtering using Clutter Density Estimation for Multiple Target Tracking

Abstract

Gaussian mixture probability hypothesis density (GM-PHD) filtering often assumes a uniform distribution of clutter in the observation area. However, in practice, clutter is often unknown and non-uniform, necessitating accurate estimation of its spatial distribution, non-uniformity, and temporal variations. To address this problem, we proposed a modified GM-PHD filtering method with clutter density estimation for multiple target tracking. In the proposed method, first, potential target measurements within the tracking gate are eliminated to obtain the clutter measurement set. Next, the clutter density around each target is estimated. Finally, the estimated clutter density is incorporated into GM-PHD filtering, to estimate the target state and clutter density in complex clutter environments. Simulation results demonstrated that the proposed filtering method improves the performance of the GM-PHD filter in multi-target tracking scenarios with unknown clutter density.