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

[+] Author and Article Information
Tao Yang

Coupang Global LLC,
Seattle, WA 98101
e-mail: paulsameyt@gmail.com

Prashant G. Mehta

Coordinated Science Lab,
Department of Mechanical
Science and Engineering,
University of Illinois at Ubana-Champaign,
Urbana, IL 61801
e-mail: mehtapg@illinois.edu

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received February 15, 2017; final manuscript received August 25, 2017; published online November 8, 2017. Assoc. Editor: Tarunraj Singh.

J. Dyn. Sys., Meas., Control 140(3), 030905 (Nov 08, 2017) (14 pages) Paper No: DS-17-1089; 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 nontarget-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 (MTT) applications.

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Figures

Grahic Jump Location
Fig. 1

Simulation results of single target tracking in clutter using PDA-FPF: Comparison of estimated mean with the true trajectory

Grahic Jump Location
Fig. 2

(a) Illustration of “ghost” target in the two-sensor two-target case: The ghost appears because of incorrect data association. Simulation results for (b) scenario (1) and (c) scenario (2).

Grahic Jump Location
Fig. 3

(a) Simulation results for SIR-PF and (b) simulation results for JPDF-FPF

Grahic Jump Location
Fig. 4

(a) Plot of data association probability and (b) comparison of root-mean-square error (RMSE) with SIR-PF and JPDA-FPF

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