Interacting T-S fuzzy particle filter algorithm for transfer probability matrix of adaptive online estimation model

•The proposed algorithm can be regarded as a switching dynamical model.•A fuzzy C-regression clustering method based on maximum correntropy principle and spatial-temporal information is proposed.•The importance density function is constructed by using the interacting T-S fuzzy model.•The proposed al...

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Veröffentlicht in:Digital signal processing Jg. 110; S. 102944
Hauptverfasser: Wang, Xiao-li, Xie, Wei-xin, Li, Liang-qun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Inc 01.03.2021
Schlagworte:
Fuzzy C-regression clustering
Interacting T-S fuzzy model
Linear Matrix Inequalities
Modified strong tracking
Particle filtering
Linear Matrix Inequalities
Particle filtering
Interacting T-S fuzzy model
Fuzzy C-regression clustering
Modified strong tracking
ISSN:1051-2004, 1095-4333
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Zusammenfassung:•The proposed algorithm can be regarded as a switching dynamical model.•A fuzzy C-regression clustering method based on maximum correntropy principle and spatial-temporal information is proposed.•The importance density function is constructed by using the interacting T-S fuzzy model.•The proposed algorithm is used to deal with the state estimation problem in the maneuvering target tracking system. For the problem of inaccurate or difficult to obtain statistical characteristics of non-Gaussian noise, an interacting T-S fuzzy modeling algorithm is proposed to incorporate spatial-temporal information into particle filtering. In the proposed method, feature information is characterized by multiple semantic fuzzy sets, and the model transition probabilities are estimated by using the fuzzy set transition probabilities, which can be derived by the closeness degrees between the fuzzy sets. Furthermore, the correntropy can capture the statistical information to solve the non-Gaussian noise, thus a kernel fuzzy C-regression means (FCRM) based on correntropy and spatial-temporal information is proposed to adaptively identify the premise parameters of T-S fuzzy model, and a modified strong tracking method is used to estimate the consequence parameters. By using the proposed interacting T-S fuzzy model, an efficient importance density function is constructed for the particle filtering algorithm. Finally, the simulation results show that the tracking performance of the proposed algorithm is effective in processing non-Gaussian noise.
ISSN:1051-2004
1095-4333
DOI:10.1016/j.dsp.2020.102944