In this paper, an improved filter procedure, named the iterated extended Kalman auxiliary particle filter (APF-IEKF), is proposed. The proposed algorithm consists of an auxiliary particle filter that uses an iterated extended Kalman filter to generate the importance proposal distribution. The simulation results show that the new procedure improves the distribution of the weights in the case of accurate measurement, which mitigates the effects of particle degeneracy problem. The experimental results also illustrate that the improved particle filter is superior to the existing niters and that it has less running time than the unscented Kalman particle filter(PF-UKF). Additionally, the performance of these algorithms is compared and some reasons of performance improving of each algorithm are analyzed.%用迭代扩展卡尔曼滤波方法来生成辅助粒子滤波的重要性密度函数,得到了一种新的改进的滤波算法:迭代扩展卡尔曼辅助粒子滤波.仿真结果表明,该算法在观测量较精确的情况下改善了粒子权值分布,减轻了粒子退化现象,该算法不仅要优于已有的滤波方法,而且比无忌卡尔曼粒子滤波运行时间短.分析了各算法改进的原因及适应情况.
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