Abstract

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.

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