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Sparse system identification problems often exist in many applications, suchas echo interference cancellation, sparse channel estimation, and adaptivebeamforming. One of popular adaptive sparse system identification (ASSI)methods is adopting only one sparse least mean square (LMS) filter. However,the adoption of only one sparse LMS filter cannot simultaneously achieve fastconvergence speed and small steady-state mean state deviation (MSD). Unlike theconventional method, we propose an improved ASSI method using affinecombination of two sparse LMS filters to simultaneously achieving fastconvergence and low steady-state MSD. First, problem formulation and standardaffine combination of LMS filters are introduced. Then an approximate optimumaffine combiner is adopted for the proposed filter according to stochasticgradient search method. Later, to verify the proposed filter for ASSI, computersimulations are provided to confirm effectiveness of the proposed filter whichcan achieve better estimation performance than the conventional one andstandard affine combination of LMS filters.

Two are Better Than One: Adaptive Sparse System Identification using Affine Combination of Two Sparse Adaptive Filters