Continuous mixed p-norm adaptive algorithm with reweighted L0-norm constraint

Abstract

A continuous mixed p-norm adaptive algorithm with reweighted L0-norm constraint (RL0-CMPN) is proposed for sparse system identification. The RL0-CMPN algorithm makes full use of the advantages of the different norm. This algorithm can solve large coefficient update spread problem and reduce the slow-down effect. Besides, it is a continuous mixed p-norm adaptive algorithm. The computation complexity of the algorithm is discussed. Finally, the algorithm is compared with some exist adaptive filtering algorithms in different signal-tonoise ratio (SNR). Theoretical analysis combined with experimental simulations show that the algorithm can achieve better tracking speed, lower steady state error and anti-noise performance.