Open AccessClustering Gaussian mixture reduction algorithm based on fuzzy adaptive resonance theory for extended target tracking
Author(s) -
Zhang Yongquan,
Ji Hongbing
Publication year - 2014
Publication title -
iet radar, sonar and navigation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.489
H-Index - 82
eISSN - 1751-8792
pISSN - 1751-8784
DOI - 10.1049/iet-rsn.2013.0254
Subject(s) - cluster analysis , reduction (mathematics) , gaussian , fuzzy logic , tracking (education) , fuzzy clustering , algorithm , adaptive resonance theory , artificial intelligence , computer science , dimensional reduction , pattern recognition (psychology) , mathematics , physics , psychology , pedagogy , geometry , quantum mechanics , mathematical physics
This study presents a global Gaussian mixture reduction (GMR) algorithm via clustering, which is based on a fuzzy adaptive resonance theory (FART) neural network architecture. Therefore the authors call the proposed algorithm as GMR based on the fuzzy ART (GMR‐FART) in this study. The architecture of GMR‐FART is similar to that of the FART, however, its choice function, match function and learning update equations are characterised by features of Gaussian mixture (GM). The proposed algorithm automatically forms categories (i.e. the reduced GM components) via a feedback mechanism. The performance of GMR‐FART is evaluated by the normalised integrated squared distance measure which describes the deviation between the original and the reduced GM. The proposed algorithm is tested on both one‐dimensional (1D) and 4D simulation examples, and the results show that the proposed algorithm can accurately approximate the original mixture and requires less computational burden, and is useful in extended target tracking.