Robust multi-sensor Generalized Labeled Multi-Bernoulli filter

TL;DR

This paper introduces a robust multi-sensor filtering algorithm that jointly estimates target trajectories and clutter parameters, outperforming existing methods when detection profiles and clutter rates are unknown or time-varying.

Contribution

It combines MS-GLMB and CPHD filters with joint estimation of detection probability and clutter rate, enabling adaptive and robust multi-sensor tracking.

Findings

Achieves near-optimal performance with true clutter and detection parameters.

Outperforms other filters under unknown, time-varying detection and clutter conditions.

Learns background parameters on-the-fly for improved robustness.

Abstract

This paper proposes an efficient and robust algorithm to estimate target trajectories with unknown target detection profiles and clutter rates using measurements from multiple sensors. In particular, we propose to combine the multi-sensor Generalized Labeled Multi-Bernoulli (MS-GLMB) filter to estimate target trajectories and robust Cardinalized Probability Hypothesis Density (CPHD) filters to estimate the clutter rates. The target detection probability is augmented to the filtering state space for joint estimation. Experimental results show that the proposed robust filter exhibits near-optimal performance in the sense that it is comparable to the optimal MS-GLMB operating with true clutter rate and detection probability. More importantly, it outperforms other studied filters when the detection profile and clutter rate are unknown and time-variant. This is attributed to the ability of the robust filter to learn the background parameters on-the-fly.