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

[+] Author and Article Information
Tao Yang

Data Scientist, Coupang Global LLC

Prashant Mehta

Associate Professor, Coordinated Science Lab, Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign

1Corresponding author.

ASME doi:10.1115/1.4037781 History: Received February 15, 2017; Revised August 25, 2017


This paper is concerned with the problem of tracking single or multiple targets with multiple non-target specific observations (measurements). For such filtering problems with data association uncertainty, a novel feedback control-based particle filter algorithm is introduced. The algorithm is referred to as the probabilistic data association-feedback particle filter (PDA-FPF). The proposed filter is shown to represent a generalization - to the nonlinear non-Gaussian case of the classical Kalman filter-based probabilistic data association filter (PDAF). One remarkable conclusion is that the proposed PDA-FPF algorithm retains the error-based feedback structure of the classical PDAF algorithm, even in the nonlinear non-Gaussian case. The theoretical results are illustrated with the aid of numerical examples motivated by multiple target tracking applications.

Copyright (c) 2017 by ASME
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