标题:A Gradient-Based Adaptive-step Algorithm for Magnitude Squared Coherence Spectral Estimation
作者:Gao Zhifeng; Cheng Yun
通讯作者:Gao, ZF
作者机构:[Gao Zhifeng; Cheng Yun] Shandong Univ, Sch Mech Elect & Informat Engn, Weihai, Peoples R China.
会议名称:IEEE 3rd International Conference on Signal and Image Processing (ICSIP)
会议日期:JUL 13-15, 2018
来源:2018 IEEE 3RD INTERNATIONAL CONFERENCE ON SIGNAL AND IMAGE PROCESSING (ICSIP)
出版年:2018
页码:347-351
关键词:magnitude squared coherence; adaptive step; frequency estimation;; canonical correlation analysis
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 SNIP is avoided.
收录类别:CPCI-S
资源类型:会议论文
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