A Particle Multi-Target Tracker for Superpositional Measurements using Labeled Random Finite Sets

TL;DR

This paper introduces a novel multi-target tracking method for superpositional measurements using labeled RFS, combining advanced filtering techniques to efficiently estimate target states in challenging radar scenarios.

Contribution

It develops a labeled RFS-based Bayesian filter for superpositional measurements and proposes an efficient sampling strategy using SA-CPHD and LMB densities.

Findings

Effective in tracking closely spaced targets

Handles low signal-to-noise ratio scenarios

Demonstrates improved accuracy over existing methods

Abstract

In this paper we present a general solution for multi-target tracking with superpositional measurements. Measurements that are functions of the sum of the contributions of the targets present in the surveillance area are called superpositional measurements. We base our modelling on Labeled Random Finite Set (RFS) in order to jointly estimate the number of targets and their trajectories. This modelling leads to a labeled version of Mahler's multi-target Bayes filter. However, a straightforward implementation of this tracker using Sequential Monte Carlo (SMC) methods is not feasible due to the difficulties of sampling in high dimensional spaces. We propose an efficient multi-target sampling strategy based on Superpositional Approximate CPHD (SA-CPHD) filter and the recently introduced Labeled Multi-Bernoulli (LMB) and Vo-Vo densities. The applicability of the proposed approach is verified through simulation in a challenging radar application with closely spaced targets and low signal-to-noise ratio.