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.

针对非高斯噪声统计特性不准确或难以获得的问题，提出了一种交互的TS模糊建模算法，将时空信息纳入粒子滤波。在该方法中，特征信息具有多个语义模糊集的特征，并利用模糊集的转移概率来估计模型的转移概率，该概率可以通过模糊集之间的接近度来推导。此外，由于熵能捕获统计信息以解决非高斯噪声，因此提出了一种基于熵和时空信息的核模糊C回归方法（FCRM）来自适应地识别TS模糊模型的前提参数，改进的强跟踪方法用于估计结果参数。通过使用所提出的交互TS模糊模型，为粒子滤波算法构建了有效的重要性密度函数。最后，仿真结果表明，该算法在非高斯噪声处理中具有良好的跟踪性能。