Multipath Extended Target Tracking with Labeled Random Finite Sets

TL;DR

This paper introduces a novel Bayesian filtering approach using labeled random finite sets to improve extended target tracking in complex multipath radar environments, enabling accurate trajectory estimation without heuristic post-processing.

Contribution

The paper develops the MPET-GLMB filter that jointly models target existence, measurement partitioning, and associations, with a Gibbs sampling implementation for efficiency and an adaptive birth model.

Findings

Outperforms existing methods in accuracy and robustness

Effective in simulated and real-world automotive radar scenarios

Maintains trajectory continuity in multipath conditions

Abstract

High-resolution radar sensors are critical for autonomous systems but pose significant challenges to traditional tracking algorithms due to the generation of multiple measurements per object and the presence of multipath effects. Existing solutions often rely on the point target assumption or treat multipath measurements as clutter, whereas current extended target trackers often lack the capability to maintain trajectory continuity in complex multipath environments. To address these limitations, this paper proposes the multipath extended target generalized labeled multi-Bernoulli (MPET-GLMB) filter. A unified Bayesian framework based on labeled random finite set theory is derived to jointly model target existence, measurement partitioning, and the association between measurements, targets, and propagation paths. This formulation enables simultaneous trajectory estimation for both targets and reflectors without requiring heuristic post-processing. To enhance computational efficiency, a joint prediction and update implementation based on Gibbs sampling is developed. Furthermore, a measurement-driven adaptive birth model is introduced to initialize tracks without prior knowledge of target positions. Experimental results from simulated scenarios and real-world automotive radar data demonstrate that the proposed filter outperforms state-of-the-art methods, achieving superior state estimation accuracy and robust trajectory maintenance in dynamic multipath environments.