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A Novel FEM Based T-S Fuzzy Particle Filtering for Bearings-Only Maneuvering Target Tracking

机译：一种基于有限元的新型T-S模糊粒子滤波仅用于方位目标跟踪

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摘要

In this paper, we propose a novel fuzzy expectation maximization (FEM) based Takagi-Sugeno (T-S) fuzzy particle filtering (FEMTS-PF) algorithm for a passive sensor system. In order to incorporate target spatial-temporal information into particle filtering, we introduce a T-S fuzzy modeling algorithm, in which an improved FEM approach is proposed to adaptively identify the premise parameters, and the model probability is adjusted by the premise membership functions. In the proposed FEM, the fuzzy parameter is derived by the fuzzy C-regressive model clustering method based on entropy and spatial-temporal characteristics, which can avoid the subjective influence caused by the artificial setting of the initial value when compared to the traditional FEM. Furthermore, using the proposed T-S fuzzy model, the algorithm samples particles, which can effectively reduce the particle degradation phenomenon and the parallel filtering, can realize the real-time performance of the algorithm. Finally, the results of the proposed algorithm are evaluated and compared to several existing filtering algorithms through a series of Monte Carlo simulations. The simulation results demonstrate that the proposed algorithm is more precise, robust and that it even has a faster convergence rate than the interacting multiple model unscented Kalman filter (IMMUKF), interacting multiple model extended Kalman filter (IMMEKF) and interacting multiple model Rao-Blackwellized particle filter (IMMRBPF).
机译：在本文中，我们提出了一种基于模糊期望最大化（FEM）的新型Takagi-Sugeno（T-S）模糊粒子滤波（FEMTS-PF）算法，用于无源传感器系统。为了将目标时空信息纳入粒子滤波，我们引入了一种T-S模糊建模算法，该算法提出了一种改进的有限元方法来自适应地识别前提参数，并通过前提隶属度函数来调整模型概率。在提出的有限元方法中，模糊参数是基于熵和时空特征的模糊C-回归模型聚类方法得出的，与传统的有限元方法相比，可以避免人为设置初始值而造成的主观影响。此外，利用提出的T-S模糊模型对该算法进行采样，可以有效地减少粒子退化现象和并行滤波，可以实现算法的实时性。最后，通过一系列的蒙特卡洛模拟对提出的算法的结果进行评估，并与几种现有的滤波算法进行比较。仿真结果表明，与交互多模型无味卡尔曼滤波器（IMMUKF），交互多模型扩展卡尔曼滤波器（IMMEKF）和交互多模型Rao-Blackwellized相比，该算法更加精确，鲁棒，收敛速度更快。粒子过滤器（IMMRBPF）。

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