Nonnegative least mean mixed-norm algorithm: Analysis and performance

Adaptive filter has been successfully considered for the system identifications. Under some circumstances, due to the unknown system's intrinsic characteristics, nonnegativity is an desired constraint imposed on the parameters to be estimated, which will affect the performance of devised adaptive filtering. This paper studies a nonnegative least mean mixed-norm algorithm (NNLMMN) with fourth-order moment error and square-order error, which are jointly considered and devised to design a new cost function for dealing with the system identification problems with a nonnegativity constraint in mixed Gaussian noise environment. We also provide the theoretical behavior prediction of the mean weight, the transient analysis and steady-state excess mean square error (EMSE) of the NNLMMN algorithm. Experiment results show that the superior performance of the NNLMMN algorithm and verify the effectiveness of the presented theory analysis. (C) 2021 Elsevier Inc. All rights reserved.