MUSIC algorithm applied to Advanced EMI sensors data for UXO classification

Economou, DP; Shubitidze, F; Barrowes, B; Uzunoglu, NK

dc.contributor.author	Economou, DP	en
dc.contributor.author	Shubitidze, F	en
dc.contributor.author	Barrowes, B	en
dc.contributor.author	Uzunoglu, NK	en
dc.date.accessioned	2014-03-01T02:47:25Z
dc.date.available	2014-03-01T02:47:25Z
dc.date.issued	2011	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/33132
dc.subject	Eigenvalues	en
dc.subject	Eigenvectors	en
dc.subject	Electromagnetic Induction	en
dc.subject	Magnetic Field	en
dc.subject	Next Generation	en
dc.subject.other	Data matrices	en
dc.subject.other	Eigenvalues	en
dc.subject.other	Electromagnetic induction sensors	en
dc.subject.other	EMI Sensors	en
dc.subject.other	IT project	en
dc.subject.other	matrix	en
dc.subject.other	Multi-static	en
dc.subject.other	Multiple signal classification algorithm	en
dc.subject.other	MUSIC algorithms	en
dc.subject.other	Noise subspace	en
dc.subject.other	Signal sub-space	en
dc.subject.other	Source location	en
dc.subject.other	Subsurface metallic targets	en
dc.subject.other	UXO classification	en
dc.subject.other	Algorithms	en
dc.subject.other	Eigenvalues and eigenfunctions	en
dc.subject.other	Electromagnetic induction	en
dc.subject.other	Magnetic fields	en
dc.subject.other	Sensors	en
dc.subject.other	Wavelet analysis	en
dc.subject.other	Computer music	en
dc.title	MUSIC algorithm applied to Advanced EMI sensors data for UXO classification	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICEAA.2011.6046514	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICEAA.2011.6046514	en
heal.identifier.secondary	6046514	en
heal.publicationDate	2011	en
heal.abstract	The multiple signal classification (MUSIC) algorithm, that utilizes next generation electromagnetic induction (EMI) sensor, multi static response (MRS) data matrix's eigenvector's and eigenvalues, is employed for estimating number of subsurface metallic targets and pinpointing their location. The method divides MRS matrix data eigenvectors into two groups: the noise and signal subspaces. It projects the estimated EM signal into the noise subspace and utilizes the fact that the modeled magnetic field for each actual source location is orthogonal to the noise subspace. Data are presented for demonstrating the effectiveness of the method. © 2011 IEEE.	en
heal.journalName	Proceedings - 2011 International Conference on Electromagnetics in Advanced Applications, ICEAA'11	en
dc.identifier.doi	10.1109/ICEAA.2011.6046514	en
dc.identifier.spage	1160	en
dc.identifier.epage	1163	en