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dc.contributor.authorHasan, Ali A.
dc.contributor.authorAzimi-Sadjadi, Mahmood R.
dc.contributor.authorHasan, Mohammed A.
dc.date.accessioned2007-01-03T04:18:34Z
dc.date.available2007-01-03T04:18:34Z
dc.date.issued2000
dc.descriptionIncludes bibliographical references.
dc.description.abstractSubspace methods such as MUSIC, Minimum Norm, and ESPRIT have gained considerable attention due to their superior performance in sinusoidal and direction-of-arrival (DOA) estimation, but they are also known to be of high computational cost. In this paper, new fast algorithms for approximating signal and noise subspaces and that do not require exact eigendecomposition are presented. These algorithms approximate the required subspace using rational and power-like methods applied to the direct data or the sample covariance matrix. Several ESPRIT- as well as MUSIC-type methods are developed based on these approximations. A substantial computational saving can be gained comparing with those associated with the eigendecomposition-based methods. These methods are demonstrated to have performance comparable to that of MUSIC yet will require fewer computation to obtain the signal subspace matrix.
dc.format.mediumborn digital
dc.format.mediumarticles
dc.identifier.bibliographicCitationHasan, Mohammed A., Mahmood R. Azimi-Sadjadi, and Ali A. Hasan, Rational Invariant Subspace Approximations with Applications, IEEE Transactions on Signal Processing 48, no. 11 (November 2000): 3032-3041.
dc.identifier.urihttp://hdl.handle.net/10217/1012
dc.languageEnglish
dc.publisherColorado State University. Libraries
dc.publisher.originalIEEE
dc.relation.ispartofFaculty Publications - Department of Electrical and Computer Engineering
dc.rights©2000 IEEE
dc.subjectMUSIC
dc.subjectrational subspace approximation
dc.subjectDOA
dc.subjectESPRIT
dc.subjectpower methods
dc.subjectfrequency estimation
dc.subjectminimum norm
dc.titleRational invariant subspace approximations with applications
dc.typeText


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