The Trajectory PHD Filter for Coexisting Point and Extended Target Tracking

TL;DR

This paper introduces a general trajectory PHD filter capable of estimating trajectories of coexisting point and extended targets using a novel derivation and efficient implementation, validated through simulations and experiments.

Contribution

It presents a new trajectory PHD filter that handles both point and extended targets without relying on probability generating functionals, improving classification and estimation accuracy.

Findings

Accurately classifies point and extended targets

Provides precise trajectory estimates

Reduces computational cost with L-scan approximation

Abstract

This paper develops a general trajectory probability hypothesis density (TPHD) filter, which uses a general density for target-generated measurements and is able to estimate trajectories of coexisting point and extended targets. First, we provide a derivation of this general TPHD filter based on finding the best Poisson posterior approximation by minimizing the Kullback-Leibler divergence, without using probability generating functionals. Second, we adopt an efficient implementation of this filter, where Gaussian densities correspond to point targets and Gamma Gaussian Inverse Wishart densities for extended targets. The L-scan approximation is also proposed as a simplified version to mitigate the huge computational cost. Simulation and experimental results show that the proposed filter is able to classify targets correctly and obtain accurate trajectory estimation.