Multiple Extended Target Tracking with Labelled Random Finite Sets

TL;DR

This paper introduces a novel labelled RFS-based algorithm for tracking multiple extended targets, capable of estimating target count, trajectories, and extents, outperforming existing methods in cluttered environments.

Contribution

It develops a new GLMB/LMB-based tracking algorithm for extended targets using GGIW distributions, improving estimation accuracy over existing filters.

Findings

Enhanced tracking accuracy demonstrated in simulations.

Better estimation of target extents and trajectories.

Outperforms extended target CPHD filter in clutter.

Abstract

Targets that generate multiple measurements at a given instant in time are commonly known as extended targets. These present a challenge for many tracking algorithms, as they violate one of the key assumptions of the standard measurement model. In this paper, a new algorithm is proposed for tracking multiple extended targets in clutter, that is capable of estimating the number of targets, as well the trajectories of their states, comprising the kinematics, measurement rates and extents. The proposed technique is based on modelling the multi-target state as a generalised labelled multi-Bernoulli (GLMB) random finite set (RFS), within which the extended targets are modelled using gamma Gaussian inverse Wishart (GGIW) distributions. A cheaper variant of the algorithm is also proposed, based on the labelled multi-Bernoulli (LMB) filter. The proposed GLMB/LMB-based algorithms are compared with an extended target version of the cardinalised probability hypothesis density (CPHD) filter, and simulation results show that the (G)LMB has improved estimation and tracking performance.