噪声鲁棒变步长LMS算法及其在OFDM水声信道均衡中的应用

Aiming at the problem of poor performance in least mean square (LMS) algorithm under low signal to noise ratio (SNR) environments, a noise robust variable step-size LMS (NRVSLMS) algorithm is proposed. By combining the improved double sigmoid function and the error signal autocorrelation function, the NRVSLMS algorithm dynamically adjusts its step-size during the iteration process to solve the problem that the convergence speed, the tracking performance and the steady-state performance are contradictory in the traditional LMS algorithm. Theoretical analysis and simulation results show that the NRVSLMS algorithm has stronger anti-noise ability, faster tracking speed and better steady state performance compared with other variable step-size algorithms. The proposed algorithm is applied to orthogonal frequency division multiplexing (OFDM) underwater acoustic communications, the proposed equalization method base on the NRVSLMS algorithm improves the performance in terms of the bit error rate (BER) and mean square error (MSE) sufficiently in comparison with the existing ones.