动态灰色聚类自适应的H滤波算法

Dynamic Grey Clustering Adaptive H Filtering Algorithm

  • 摘要: 为解决H∞滤波器结构参数随时间增长而发散的问题,提出了一种动态灰色聚类自适应的H∞滤波新算法.实时估计出系统噪声方差矩阵和量测噪声方差矩阵,对状态变量进行灰色聚类,并对滤波矩阵和增益矩阵进行实时自适应调整,计算出状态向量的递推估计值.仿真结果表明:H∞滤波新算法与传统H∞滤波算法和基本Kalman滤波算法相比,滤波精度相当,输出曲线光滑,滤波器的结构参数在10 s内稳定且收敛.改进后的新算法避免了计算值的发散,鲁棒性强.

     

    Abstract: To solve the problem that parameters of H filter increase with time,a new adaptive H filter algorithm is proposed.Based on real-time estimation of noise matrix and grey clustering of state variable,filter or gain matrix is modulated so as to get an estimation of the state vector.Simulation showed that,using the new algorithm,compared with the H or Kalman filter, expected precision could be achieved and parameters could be made stable in 10 s.The improved algorithm can avoid spreading of computation and has excellent robustness.

     

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