Revision as of 04:18, 17 March 2024 edit VulcanSphere (talk \| contribs) Extended confirmed users 22,132 edits m Adding local short description: "Area of research in signal processing", overriding Wikidata description "wide area of research in the field of signal processing that extends from the simplest form of 1 dimensional line arrays to 2 and 3 dimensional array geometries" Tag: Shortdesc helper ← Previous edit		Revision as of 18:57, 13 October 2024 edit undo Plasticwonder (talk \| contribs) Extended confirmed users, Pending changes reviewers, Rollbackers 14,422 edits unencyclopedic Next edit →
Line 92: ==== Subspace-based technique ==== Many spectral methods in the past have called upon the spectral decomposition of a covariance matrix to carry out the analysis. A ~~very important~~ breakthrough came about when the eigen-structure of the covariance matrix was explicitly invoked, and its intrinsic properties were directly used to provide a solution to an underlying estimation problem for a given observed process. A class of spatial spectral estimation techniques is based on the eigen-value decomposition of the spatial covariance matrix. The rationale behind this approach is that one wants to emphasize the choices for the steering vector a(θ) which correspond to signal directions. The method exploits the property that the directions of arrival determine the eigen structure of the matrix.<br> The tremendous interest in the subspace based methods is mainly due to the introduction of the [[MUSIC (algorithm)\|MUSIC (Multiple Signal Classification)]] algorithm. MUSIC was originally presented as a DOA estimator, then it has been successfully brought back to the spectral analysis/system identification problem with its later development.<ref name="ref2"/><ref name="ref6"/><ref name="ref5"/>

Array processing: Difference between revisions