Reduced-Complexity Constrained Recursive Least-Squares Adaptive Filtering Algorithm

摘要

A linearly-constrained recursive least-squares adaptive filtering algorithm based on the method of weighting and the dichotomous coordinate descent (DCD) iterations is proposed. The method of weighting is employed to incorporate the linear constraints into the least-squares problem. The normal equations of the resultant unconstrained least-squares problem are then solved using the DCD iterations. The proposed algorithm has a significantly smaller computational complexity than the previously proposed constrained recursive least square (CRLS) algorithm while delivering convergence performance on par with CRLS. The effectiveness of the proposed algorithm is demonstrated by simulation examples.