Constrained Least Mean M-Estimation Adaptive Filtering Algorithm

In many applications, the constrained adaptive filtering algorithm has been widely studied. The classical constrained LMS algorithm is widely used because of its low computational complexity. However, the performance of constrained LMS algorithm will degrade under correlated input or non-Gaussian noise. In order to overcome this defect, this brief proposes a constrained least mean M-estimation (CLMM) algorithm, which uses the M-estimation cost function for the constrained adaptive filter. Compared with the previous algorithms for non-Gaussian noise, such as constrained maximum correntropy criterion (CMCC) algorithm and constrained minimum error entropy (CMEE) algorithm, the proposed CLMM algorithm has lower computational complexity and better steady-state performance. In addition, the step-size range is determined by analyzing the mean square stability, which ensures the stability of the proposed CLMM algorithm. Simulation results illustrate that the proposed CLMM algorithm has better steady-state performance than previous algorithms in non-Gaussian noises with multi-peak distribution.