Convex combination of adaptive filters for a variable tap-length LMS algorithm

A convex combination of adaptive filters is utilized to improve the performance of a variable tap-length least-mean-square (LMS) algorithm in a low signal-to-noise environment (SNR <= 0 dB). As shown by our simulations, the adaptation of the tap-length in the variable tap-length LMS algorithm is highly affected by the parameter choice and the noise level. Combination approaches can improve such adaptation by exploiting advantages of parallel adaptive filters with different parameters. Simulation results support the good properties of the proposed method.