Approach for Adaptive Filter of Systems with Random Changing Structures
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摘要: 提出了含有相关和非相关两类特征参数的结构随机跳变系统自适应滤波方法,并采用改进了的一种基于随机采样技术的自举滤波算法,其对于非线性非高斯问题具有很强的适应性,很好地克服了传统滤波算法在解决此类问题时所存在的"基础结构失真"的缺陷,从而提高了滤波算法的整体估计性能.在"闪烁"噪声环境里,测量信息存在随机中断的导弹制导问题的仿真结果,验证了本方法的有效性.Abstract: An approach was given for adaptive filter of systems with random changing structures containing correlative or non-correlative characteristic parameters. An improved bootstrap filtering algorithm based on random sampling, which adapts quite well to the nonlinear/non-Gaussian problems, was adopted to avoid "basic structure distortion" caused by conventional filtering algorithm and to increase the estimation precision of the total filtering algorithm. Simulation results about missile guidance problems where measurement information is randomly interrupted in "twinkling" noise environment proved the validity of the approach.
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