Highest Probability Data Association for Multi-Target Particle Filtering with Nonlinear Measurements

Da Sol KIM; Taek Lyul SONG; Darko MUŠICKI

doi:10.1587/transcom.E96.B.281

Highest Probability Data Association for Multi-Target Particle Filtering with Nonlinear Measurements

Da Sol KIM, Taek Lyul SONG, Darko MUŠICKI

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In this paper, we propose a new data association method termed the highest probability data association (HPDA) and apply it to real-time recursive nonlinear tracking in heavy clutter. The proposed method combines the probabilistic nearest neighbor (PNN) with a modified probabilistic strongest neighbor (PSN) approach. The modified PSN approach uses only the rank of the measurement amplitudes. This approach is robust as exact shape of amplitude probability density function is not used. In this paper, the HPDA is combined with particle filtering for nonlinear target tracking in clutter. The measurement with the highest measurement-to-track data association probability is selected for track update. The HPDA provides the track quality information which can be used in for the false track termination and the true track confirmation. It can be easily extended to multi-target tracking with nonlinear particle filtering. The simulation studies demonstrate the HPDA functionality in a hostile environment with high clutter density and low target detection probability.

Publication: IEICE TRANSACTIONS on Communications Vol.E96-B No.1 pp.281-290

Publication Date: 2013/01/01

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Online ISSN: 1745-1345

DOI: 10.1587/transcom.E96.B.281

Type of Manuscript: PAPER

Category: Sensing

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Da Sol KIM, Taek Lyul SONG, Darko MUŠICKI, "Highest Probability Data Association for Multi-Target Particle Filtering with Nonlinear Measurements" in IEICE TRANSACTIONS on Communications, vol. E96-B, no. 1, pp. 281-290, January 2013, doi: 10.1587/transcom.E96.B.281.
Abstract: In this paper, we propose a new data association method termed the highest probability data association (HPDA) and apply it to real-time recursive nonlinear tracking in heavy clutter. The proposed method combines the probabilistic nearest neighbor (PNN) with a modified probabilistic strongest neighbor (PSN) approach. The modified PSN approach uses only the rank of the measurement amplitudes. This approach is robust as exact shape of amplitude probability density function is not used. In this paper, the HPDA is combined with particle filtering for nonlinear target tracking in clutter. The measurement with the highest measurement-to-track data association probability is selected for track update. The HPDA provides the track quality information which can be used in for the false track termination and the true track confirmation. It can be easily extended to multi-target tracking with nonlinear particle filtering. The simulation studies demonstrate the HPDA functionality in a hostile environment with high clutter density and low target detection probability.
URL: https://globals.ieice.org/en_transactions/communications/10.1587/transcom.E96.B.281/_p

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@ARTICLE{e96-b_1_281,
author={Da Sol KIM, Taek Lyul SONG, Darko MUŠICKI, },
journal={IEICE TRANSACTIONS on Communications},
title={Highest Probability Data Association for Multi-Target Particle Filtering with Nonlinear Measurements},
year={2013},
volume={E96-B},
number={1},
pages={281-290},
abstract={In this paper, we propose a new data association method termed the highest probability data association (HPDA) and apply it to real-time recursive nonlinear tracking in heavy clutter. The proposed method combines the probabilistic nearest neighbor (PNN) with a modified probabilistic strongest neighbor (PSN) approach. The modified PSN approach uses only the rank of the measurement amplitudes. This approach is robust as exact shape of amplitude probability density function is not used. In this paper, the HPDA is combined with particle filtering for nonlinear target tracking in clutter. The measurement with the highest measurement-to-track data association probability is selected for track update. The HPDA provides the track quality information which can be used in for the false track termination and the true track confirmation. It can be easily extended to multi-target tracking with nonlinear particle filtering. The simulation studies demonstrate the HPDA functionality in a hostile environment with high clutter density and low target detection probability.},
keywords={},
doi={10.1587/transcom.E96.B.281},
ISSN={1745-1345},
month={January},}

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TY - JOUR
TI - Highest Probability Data Association for Multi-Target Particle Filtering with Nonlinear Measurements
T2 - IEICE TRANSACTIONS on Communications
SP - 281
EP - 290
AU - Da Sol KIM
AU - Taek Lyul SONG
AU - Darko MUŠICKI
PY - 2013
DO - 10.1587/transcom.E96.B.281
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E96-B
IS - 1
JA - IEICE TRANSACTIONS on Communications
Y1 - January 2013
AB - In this paper, we propose a new data association method termed the highest probability data association (HPDA) and apply it to real-time recursive nonlinear tracking in heavy clutter. The proposed method combines the probabilistic nearest neighbor (PNN) with a modified probabilistic strongest neighbor (PSN) approach. The modified PSN approach uses only the rank of the measurement amplitudes. This approach is robust as exact shape of amplitude probability density function is not used. In this paper, the HPDA is combined with particle filtering for nonlinear target tracking in clutter. The measurement with the highest measurement-to-track data association probability is selected for track update. The HPDA provides the track quality information which can be used in for the false track termination and the true track confirmation. It can be easily extended to multi-target tracking with nonlinear particle filtering. The simulation studies demonstrate the HPDA functionality in a hostile environment with high clutter density and low target detection probability.
ER -