‐norm feature least mean square algorithm

Abstract

In many practical applications, systems and signals show energy concentration in a few coefficients. This prior knowledge can often be incorporated into algorithms designed for tasks such as compressive sensing and system identification. This Letter proposes a new least mean square (LMS)‐based algorithm that exploits the hidden sparsity of the system that the adaptive filter intends to estimate. The algorithm minimises the ‐norm of a linear transformation of the coefficient vector, using the minimum distortion principle. Simulation results demonstrate good performance of the proposed algorithm with respect to the LMS algorithm. In addition, a stochastic model of the advanced algorithm is proposed, which provides accurate mean‐square deviation and mean‐square error predictions.