Trajectory PHD Filter with Unknown Detection Profile and Clutter Rate

TL;DR

This paper introduces a robust trajectory PHD filter that adaptively learns unknown detection profiles and clutter rates, improving multi-target tracking accuracy with computationally efficient approximations.

Contribution

It develops the R-TPHD filter that adaptively estimates detection and clutter parameters, along with a Beta-Gaussian mixture implementation and an L-scan approximation for efficiency.

Findings

The R-TPHD filter effectively learns unknown detection profiles and clutter rates.

The BG-R-TPHD filter provides a practical implementation with improved accuracy.

The L-scan approximation reduces computational complexity significantly.

Abstract

In this paper, we derive the robust TPHD (R-TPHD) filter, which can adaptively learn the unknown detection profile history and clutter rate. The R-TPHD filter is derived by obtaining the best Poisson posterior density approximation over trajectories on hybrid and augmented state space by minimizing the Kullback-Leibler divergence (KLD). Because of the huge computational burden and the short-term stability of the detection profile, we also propose the R-TPHD filter with unknown detection profile only at current time as an approximation. The Beta-Gaussian mixture model is proposed for the implementation, which is referred to as the BG-R-TPHD filter and we also propose a L-scan approximation for the BG-R-TPHD filter, which possesses lower computational burden.