标题：Robust Constrained Recursive Least P-Power Algorithm for Adaptive Filtering
作者：Sun, Jiajun; Peng, Siyuan; Liu, Qinglai; Zhao, Ruijie; Lin, Zhiping
作者机构：[Sun, Jiajun; Peng, Siyuan; Liu, Qinglai; Lin, Zhiping] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore, Singapore.; [Zhao, Ruijie] Shandong 更多
会议名称：23rd IEEE International Conference on Digital Signal Processing (DSP)
会议日期：NOV 19-21, 2018
来源：2018 IEEE 23RD INTERNATIONAL CONFERENCE ON DIGITAL SIGNAL PROCESSING (DSP)
关键词：CRLP; LMP; non-Gaussian noises
摘要：In this paper, we develop a novel constrained adaptive filtering algorithm called constrained recursive least p-power (CRLP) algorithm, which incorporates a set of linear constraints into the least mean p-power error (LMP) criterion to solve a constrained optimization problem directly. Compared with the conventional constrained adaptive filtering algorithms including constrained least mean square (CLMS), constrained recursive least square (CRLS) and constrained least mean p-power (CLMP), CRLP can achieve better performance under non Gaussian noises. Simulation results are presented to confirm the superior performance of the new algorithm.