An improved variable step Frequency-domain Block LMS algorithm with adaptive filtering

To solve the problem that the convergence performance of classical adaptive filtering algorithms is sensitive to input signal power and is hard to balance between convergence speed and steady-state misadjustment, this paper, based on the traditional Frequency-domain Block LMS (FBLMS) algorithm, presents a variable step algorithm which step with easy and reliable parameter tuning is controlled by current input signal energy and filtering error energy together. In addition, a new convergence performance indicator that is filtered-source block gain is proposed specially for block LMS algorithms during simulation. Simulation results for adaptive filtering are presented to demonstrate the performance improvements in convergence speed and steady-state misadjustment compared with other existing algorithms such as the normalized LMS (NLMS) and traditional FBLMS.