为提高变步长最小均方(LMS)自适应算法在噪声干扰下的时变时延跟踪性能,提出改进的变步长LMS自适应算法.该算法对MVSS-LMS算法进行误差均值补偿,改步长因子固定范围约束为动态变化约束；使用HB加权突出自适应滤波器权系数峰值,采用滑动窗遗忘加权减小计算复杂度.自适应时延估计仿真实验和消声水池目标被动定位试验表明:相比于参数固定条件下的MVSS-LMS算法和SVS-LMS算法,改进算法能够获得更好的时变时延跟踪性能.%In order to improve the performance of existing variable step-size LMS-type (least mean square) algorithm for tracking time-varying delay in noise interference was discussed. Based on variable step-size MVSS-LMS algorithm, the time-averaged estimate of error's autocorrelation was compensated and then the fixed range restrict of step-size was replaced by dynamic change restrict, overcoming fast attenuation of step-size because of less error relativity. Compared to another variable step-size SVS-LMS algorithm, the refrained algorithm was provided with smoother step-size variation and lower steady-state niisadjustment. HB weighted method was introduced into the algorithm to give prominence to peaks of adaptive filter's coefficient and sliding forgetting-weighted window could reduce computational complexity. The results show that, compared to MVSS-LMS and SVS-LMS algorithm being of fixed parameters, the algorithm and its HB weighted method can achieve superior performance for case of Gaussian and impulsive noise interference.
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