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

Da Sol KIM  Taek Lyul SONG  Darko MUŠICKI  

IEICE TRANSACTIONS on Communications   Vol.E96-B   No.1   pp.281-290
Publication Date: 2013/01/01
Online ISSN: 1745-1345
DOI: 10.1587/transcom.E96.B.281
Print ISSN: 0916-8516
Type of Manuscript: PAPER
Category: Sensing
data association,  track initiation,  nonlinear measurement,  multi-target tracking,  order statistics,  

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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.