Multi-Sensor Control for Multi-Target Tracking Using Cauchy-Schwarz Divergence

TL;DR

This paper introduces two novel multi-sensor control strategies for multi-target tracking using Cauchy-Schwarz divergence within a labeled RFS framework, balancing optimality and computational efficiency.

Contribution

It proposes two new multi-sensor control methods based on Cauchy-Schwarz divergence and GCI, with one being optimal and the other faster and suboptimal.

Findings

Both methods improve multi-target tracking accuracy in simulations.

The joint decision approach outperforms the independent decision method.

Simulation results validate the effectiveness of the proposed strategies.

Abstract

The paper addresses the problem of multi-sensor control for multi-target tracking via labelled random finite sets (RFS) in the sensor network systems. Based on an information theoretic divergence measure, namely Cauchy-Schwarz (CS) divergence which admits a closed form solution for GLMB densities, we propose two novel multi-sensor control approaches in the framework of generalized Covariance Intersection (GCI). The first joint decision making (JDM) method is optimal and can achieve overall good performance, while the second independent decision making (IDM) method is suboptimal as a fast realization with smaller amount of computations. Simulation in challenging situation is presented to verify the effectiveness of the two proposed approaches.