Reduced-Complexity Constrained Recursive Least-Squares Adaptive Filtering Algorithm

被引：31
作者：
Arablouei, Reza [1 ]
Dogancay, Kutluyil [1 ,2 ]
机构：
[1] Univ S Australia, Inst Telecommun Res, Mawson Lakes, SA 5095, Australia
[2] Univ S Australia, Sch Elect & Informat Engn, Mawson Lakes, SA 5095, Australia
来源：
IEEE TRANSACTIONS ON SIGNAL PROCESSING | 2012年 / 60卷 / 12期
关键词：
Constrained least-squares; dichotomous coordinate descent algorithm; linearly-constrained adaptive filtering; method of weighting;
D O I：
10.1109/TSP.2012.2217339
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
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.
引用
收藏
页码：6687 / 6692
页数：7
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