一种基于滤波参数在线辨识的鲁棒自适应滤波器

针对一类未建模动态和扰动下的非线性随机系统的状态估计问题,提出了一种基于滤波参数在线辨识的鲁棒自适应滤波器.该算法通过极小化状态估计误差的方差同时正交化相邻时刻的滤波残差,在线辨识状态预报误差和滤波残差的协方差,实现了对未建模动态和扰动的自适应动态补偿,因此对未建模扰动具有很强的鲁棒性.仿真中研究了一个非线性随机时滞系统,其参数存在缓变和突变,时滞会多次跳变,量测噪声发生了均值漂移和方差突变.算法对时滞和参数的联合估计效果令人满意.

A Robust Adaptive Filter Based on Online Filter Parameter Identification

A class of time-varying nonlinear stochastic systems subject to unmodeled dy-namics and disturbances is considered. Through online filter parameter identification, a robust adaptive filter (RAF) is proposed. The optimal filtering parameters, such as covariance of state errors and filtering residuals, are determined by minimizing the covariance of state errors and ensuring the orthogonality of the filtering residuals at two adja-cent times. The simulation example is a nonlinear time-delay stochastic system, in which mean and covariance of measurement errors are changed randomly and abruptly to simulate sensor faults. Even in such severe scenario, the RAF has strong robustness a-gainst measurement errors and shows satisfactory adaptive ability to track changes of time-delay and parameters no matter whether such changes are abrupt or slow.