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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Pisarenko harmonic decomposition ： ウィキペディア英語版 Pisarenko harmonic decomposition Pisarenko harmonic decomposition, also referred to as Pisarenko's method, is a method of frequency estimation.〔Hayes, Monson H., ''Statistical Digital Signal Processing and Modeling'', John Wiley & Sons, Inc., 1996. ISBN 0-471-59431-8.〕 This method assumes that a signal, $x(n)$, consists of $p$ complex exponentials in the presence of white noise. Because the number of complex exponentials must be known ''a priori'', it is somewhat limited in its usefulness. Pisarenko's method also assumes that $p\; +\; 1$ values of the $M\; \backslash times\; M$ autocorrelation matrix are either known or estimated. Hence, given the $(p\; +\; 1)\; \backslash times\; (p\; +\; 1)$ autocorrelation matrix, the dimension of the noise subspace is equal to one and is spanned by the eigenvector corresponding to the minimum eigenvalue. This eigenvector is orthogonal to each of the signal vectors. The frequency estimates may be determined by setting the frequencies equal to the angles of the roots of the eigenfilter :$V\_(z)\; =\; \backslash sum\_^p\; v\_(k)\; z^$ or the location of the peaks in the frequency estimation function :$\backslash hat\; P\_(e^)\; =\; \backslash frac\; \backslash mathbf\_|^2\}$, where $\backslash mathbf\_$ is the noise eigenvector and :. ==History== Vladilen Fedorovich Pisarenko originated this method in 1973 while examining the problem of estimating the frequencies of complex signals in white noise. He found that the frequencies could be derived from the eigenvector corresponding to the minimum eigenvalue of the autocorrelation matrix.〔Pisarenko, V. F. ''The retrieval of harmonics from a covariance function'' Geophysics, J. Roy. Astron. Soc., vol. 33, pp. 347-366, 1973.〕 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Pisarenko harmonic decomposition」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.