Trajectory probability hypothesis density filter

TL;DR

This paper introduces the trajectory probability hypothesis density (TPHD) filter, a novel method for estimating multiple trajectories efficiently without exhaustive measurement association, using a Gaussian mixture implementation and simulation validation.

Contribution

The paper proposes the TPHD filter for set of trajectories, providing a recursive Gaussian mixture implementation that improves trajectory estimation in multi-target tracking.

Findings

Effective trajectory estimation demonstrated through simulations

Gaussian mixture implementation enhances computational efficiency

TPHD filter outperforms traditional methods in accuracy

Abstract

This paper presents the probability hypothesis density (PHD) filter for sets of trajectories: the trajectory probability density (TPHD) filter. The TPHD filter is capable of estimating trajectories in a principled way without requiring to evaluate all measurement-to-target association hypotheses. The TPHD filter is based on recursively obtaining the best Poisson approximation to the multitrajectory filtering density in the sense of minimising the Kullback-Leibler divergence. We also propose a Gaussian mixture implementation of the TPHD recursion. Finally, we include simulation results to show the performance of the proposed algorithm.