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Sparse adaptive filtering has gained much attention due to its wideapplicability in the field of signal processing. Among the main algorithmfamilies, sparse norm constraint adaptive filters develop rapidly in recentyears. However, when applied for system identification, most priori work insparse norm constraint adaptive filtering suffers from the difficulty ofadaptability to the sparsity of the systems to be identified. To address thisproblem, we propose a novel variable p-norm constraint least mean square (LMS)algorithm, which serves as a variant of the conventional Lp-LMS algorithmestablished for sparse system identification. The parameter p is iterativelyadjusted by the gradient descent method applied to the instantaneous squareerror. Numerical simulations show that this new approach achieves betterperformance than the traditional Lp-LMS and LMS algorithms in terms ofsteady-state error and convergence rate.

Error Gradient-based Variable-Lp Norm Constraint LMS Algorithm for Sparse System Identification