标题:Robust Constrained Recursive Least P-Power Algorithm for Adaptive Filtering
作者:Sun, Jiajun; Peng, Siyuan; Liu, Qinglai; Zhao, Ruijie; Lin, Zhiping
通讯作者:Lin, ZP
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)
出版年:2018
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.
收录类别:CPCI-S
资源类型:会议论文
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