Simulating Comparison of Modified LMS Algorithms in Noise Cancellation Application

The theory of noise canceling and methods to abstract the desired signals from strong background noise using adaptive filtering are described. LMS algorithms based Sigmind function, Tongue-Like Curve ,and Sample function are compared. The simulation results show that all this algorithms can improve the ability to detect weak signals under the strong background noise. Compared with the other two algorithms, LMS algorithm based on Sample function has better performance, lower misadjustment noise, and stronger robustness against noise and disturbance.