Joint Probabilistic Data Association-Feedback Particle Filter for Multiple Target Tracking Applications

TL;DR

This paper presents a new feedback-control particle filter, JPDA-FPF, that effectively handles data association uncertainty in multiple target tracking, maintaining a feedback structure even in complex nonlinear scenarios.

Contribution

It introduces the JPDA-FPF, a novel particle filter that combines joint probabilistic data association with feedback control, extending the feedback particle filter framework to uncertain data association cases.

Findings

Retains feedback structure under data association uncertainty

Demonstrates effectiveness through numerical examples

Applicable to nonlinear multiple target tracking

Abstract

This paper introduces a novel feedback-control based particle filter for the solution of the filtering problem with data association uncertainty. The particle filter is referred to as the joint probabilistic data association-feedback particle filter (JPDA-FPF). The JPDA-FPF is based on the feedback particle filter introduced in our earlier papers. The remarkable conclusion of our paper is that the JPDA-FPF algorithm retains the innovation error-based feedback structure of the feedback particle filter, even with data association uncertainty in the general nonlinear case. The theoretical results are illustrated with the aid of two numerical example problems drawn from multiple target tracking applications.