Analysis on the adaptive filter based on LMS algorithm

This article focuses on the application of adaptive filter based on the LMS algorithm. An adaptive filter of the closed-loop system is introduced, including the elimination of interference signal, the prediction of useful signal, and the approximation of expected signal. LMS (Least Mean Square) algorithm is used to meet the optimum norm of error between estimated signal and expected signal. The structure of LMS algorithm is presented and the simulation of LMS algorithm is carried out. The results indicate that the convergence performances of LMS algorithm are prefect, and the input signal can converge to the expected signal. The application of adaptive filtering technology in this article includes the correction of channel mismatch by an adaptive linear filter, the improvement of system performance by an adaptive equalizer, and the filter of frequency signal by an adaptive notch filter. The analysis on adaptive linear filter shows that the constant channel mismatch can be corrected quite well by the correction algorithm. The analysis on adaptive equalizer shows that the error rate of system with an adaptive equalizer has significant improvement gains over that of system without an adaptive equalizer. The smaller the error rate, the larger the SNR. The relationship between error rate and multi-path loss show that the error rate is largest when the loss factor is 0.5. The analysis on adaptive notch filter shows that the interference signal with two different known frequencies can be eliminated effectively by the adaptive notch filter. The filtered signals accord with the corresponding useful signals very well.