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

Highlights•The proposed algorithm can be regarded as a switching dynamical model. But unlike popular interacting multiple model-based method, it derives the probabilistic switching model by fuzzy sets through using their closeness degree to incorporate the spatial-temporal features.•A fuzzy C-regression clustering method based on maximum correntropy principle and spatial-temporal information is proposed for parameter identification in T-S fuzzy model. Meanwhile, the consequence parameters are identified based on the modified strong tracking method.•The importance density function is constructed by using the interacting T-S fuzzy model, which contains a wealth of prior knowledge and the latest observation information, effectively improving the diversity of particles.•The proposed algorithm is used to deal with the state estimation problem in the maneuvering target tracking system.AbstractFor 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.

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