Affine shape adaptation - Revision history

en>Cydebot: Robot - Speedily moving category Feature detection to :Category:Feature detection (computer vision) per CFDS.

2014-08-09T16:17:21Z

Robot - Speedily moving category Feature detection to Category:Feature detection (computer vision) per CFDS.

← Older revision		Revision as of 18:17, 9 August 2014
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	~~'''Pisarenko harmonic decomposition''', also referred to as '''Pisarenko's method''', is a method of [[frequency estimation]].<ref>Hayes, Monson H., ''Statistical Digital Signal Processing~~ and ~~Modeling'', John Wiley & Sons, Inc., 1996. ISBN 0-471-59431-8.</ref> This method assumes~~ that ~~a signal, <math>x(n)</math>, consists of <math>p</math> [[complex exponential]]s in the presence of white noise~~. ~~Because the number~~ of ~~complex exponentials must be known ''a priori'',~~ it is ~~somewhat limited~~ in ~~its usefulness.~~		I am Oscar and I completely dig that name. He is really fond of doing ceramics but he is struggling to discover time for it. My day occupation is a meter reader. Her spouse and her live in Puerto Rico but she will have to move 1 day or an additional.<br><br>Also visit my web blog; [http://www.1a-pornotube.com/user/GCalvert http://www.1a-pornotube.com]

	~~Pisarenko's method also assumes that <math>p +~~ 1~~</math> values of the <math>M \times M</math> [[autocorrelation matrix]] are either known~~ or ~~estimated~~. ~~Hence, given the~~ <~~math~~>~~(p + 1) \times (p + 1)~~<~~/math~~> ~~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]]~~

	~~:<math>V_{min}(z) = \sum_{k=0}^p v_{min}(k) z^{-k}<~~/~~math>~~

	~~or the location of the peaks in the frequency estimation function~~

	:~~<math>\hat P_{PHD}(e^{j \omega}) = \frac{1}{\|\mathbf{e}^{H} \mathbf{v}_{min}\|^2}<~~/~~math>,~~

	~~where <math>\mathbf{v}_{min}</math> is the noise eigenvector and~~

	~~:<math>e = \begin{bmatrix}1 & e^{j \omega} & e^{j 2 \omega} & \cdots & e^{j (M-1) \omega}\end{bmatrix}^T<~~/~~math>.~~

	~~==History==~~

	[[Vladimir 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.<ref>Pisarenko, V. F. ''The retrieval of harmonics from a covariance function'' Geophysics, J. ~~Roy. Astron. Soc., vol. 33, pp. 347~~-~~366, 1973~~.~~</ref>~~

	~~==References==~~
	~~<references/>~~

	~~==See also==~~
	*[[Multiple signal classification]] (MUSIC)

	~~[[Category:Digital signal processing]~~]

en>Tpl: /* References */

2013-07-02T08:21:15Z

References

← Older revision		Revision as of 10:21, 2 July 2013
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	~~Hello~~. ~~Allow me introduce the author~~. ~~Her title is Emilia Shroyer but it~~'s ~~not~~ the ~~most feminine title out there~~. ~~Bookkeeping~~ is ~~her working day occupation now~~. ~~One~~ of [~~http://www~~.~~gaysphere.net~~/~~blog/212030 over~~ the ~~counter std test] things she loves most~~ is to ~~read comics~~ and ~~she'll be starting something else alongside with it~~. ~~California~~ is ~~exactly~~ where ~~her home~~ is ~~but she needs~~ to ~~transfer simply because~~ of ~~her family~~.		'''Pisarenko harmonic decomposition''', also referred to as '''Pisarenko's method''', is a method of [[frequency estimation]].<ref>Hayes, Monson H., ''Statistical Digital Signal Processing and Modeling'', John Wiley & Sons, Inc., 1996. ISBN 0-471-59431-8.</ref> This method assumes that a signal, <math>x(n)</math>, consists of <math>p</math> [[complex exponential]]s 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 <math>p + 1</math> values of the <math>M \times M</math> [[autocorrelation matrix]] are either known or estimated. Hence, given the <math>(p + 1) \times (p + 1)</math> 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]]

			:<math>V_{min}(z) = \sum_{k=0}^p v_{min}(k) z^{-k}</math>

			or the location of the peaks in the frequency estimation function

			:<math>\hat P_{PHD}(e^{j \omega}) = \frac{1}{\|\mathbf{e}^{H} \mathbf{v}_{min}\|^2}</math>,

			where <math>\mathbf{v}_{min}</math> is the noise eigenvector and

			:<math>e = \begin{bmatrix}1 & e^{j \omega} & e^{j 2 \omega} & \cdots & e^{j (M-1) \omega}\end{bmatrix}^T</math>.

			==History==

			[[Vladimir 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.<ref>Pisarenko, V. F. ''The retrieval of harmonics from a covariance function'' Geophysics, J. Roy. Astron. Soc., vol. 33, pp. 347-366, 1973.</ref>

			==References==
			<references/>

			==See also==
			*[[Multiple signal classification]] (MUSIC)

			[[Category:Digital signal processing]]

70.247.162.84: tweak

2012-08-20T02:52:19Z

tweak

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