标题:A Gradient-based adaptive-step algorithm for magnitude squared coherence spectral estimation
作者:Zhifeng, Gao ;Yun, Cheng
作者机构:[Zhifeng, Gao ;Yun, Cheng ] School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, China
会议名称:2018 IEEE 3rd International Conference on Signal and Image Processing, ICSIP 2018
会议日期:13 July 2018 through 15 July 2018
来源:2018 IEEE 3rd International Conference on Signal and Image Processing, ICSIP 2018
出版年:2019
页码:347-351
DOI:10.1109/SIPROCESS.2018.8600446
关键词:Adaptive step; Canonical correlation analysis; Frequency estimation; Magnitude squared coherence
摘要:Conventionally, the signal component frequencies are estimated by spectral peak search process, and suffered with the common signal mismatch problem (SMP). MVDR and CCA are typical nonparametric methods in magnitude squared coherence (MSC) spectral estimation. In this paper, a scalar cost function is developed based on the CCA MSC spectrum, where local peak indicates the estimation of signal frequency. Then, a gradient-based adaptive-step algorithm is presented to find the local peaks. In simulations, the proposed algorithm improves the frequency estimation accuracy, and SMP is avoided. © 2018 IEEE.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061811590&doi=10.1109%2fSIPROCESS.2018.8600446&partnerID=40&md5=31cb016c1c8ca3ba55a9481b99a08099
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