Open AccessRobust Bayesian partition for extended target Gaussian inverse Wishart PHD filter
Author(s) -
Zhang Yongquan,
Ji Hongbing
Publication year - 2014
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/iet-spr.2013.0150
Subject(s) - bayesian probability , gaussian , algorithm , partition (number theory) , mathematics , cluster analysis , cardinality (data modeling) , filter (signal processing) , computer science , artificial intelligence , data mining , combinatorics , physics , quantum mechanics , computer vision
Extended target Gaussian inverse Wishart PHD filter is a promising filter. However, when the two or more different sized extended targets are spatially close, the simulation results conducted by Granström et al . show that the cardinality estimate is much smaller than the true value for the separating tracks. In this study, the present authors call this phenomenon as the cardinality underestimation problem, which can be solved via a novel robust clustering algorithm, called Bayesian partition, derived by combining the fuzzy adaptive resonance theory with Bayesian theorem. In Bayesian partition, alternative partitions of the measurement set are generated by the different vigilance parameters. Simulation results show that the proposed partitioning method has better tracking performance than that presented by Granström et al., implying good application prospects.