一种新的快速自适应滤波算法的研究

Study of a new fast adaptive filtering algorithm

从信号前后时刻相关性角度出发,提出了一种新的快速自适应滤波算法,并证明了其最优权值是一种广义的维纳方程解。该算法结构简单,收敛速度快,稳态失调小,具有处理多种信号的能力,包括非相关和强相关信号,而其计算量与NLMS算法相当。仿真结果表明,对于非相关信号,新算法的稳态失调小于VS-NLMS算法, 收敛速度与参考文献已有算法相当,但快于L.E-LMS算法;对于强相关性信号,新算法的稳态失调小于NLMS 算法和DCR-LMS算法。

A new fast adaptive filtering algorithm was presented by using the correlations between the signal＇s former and latter sampling times. The proof of the new algorithm was also presented, which showed that its optimal weight vector was the solution of generalized Wiener equation. The new algorithm was of simple structure, fast convergence, less stable maladjustment. It had the ability of dealing with many signals, including noncorrelation signal and strong correlation signal. However, its computational complexity was comparable to that of NLMS algorithm. Simulation results show that for noncorrelation signal, the stable maladjustment of the proposed algorithm is less than that of VS-NLMS algorithm, and its convergence is comparable to that of the algorithm proposed in reference but faster than that of L.E-LMS algorithm. For high correlation signal, its performance is superior to those of NLMS algorithm and DCR-LMS algorithm.