On Generalized Eigenvector Space For Target Detection in Reduced Dimensions


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GÜVENSEN G. M. , CANDAN Ç., Koc S., ORGUNER U.

IEEE International Radar Conference (RadarCon), Virginia, United States Of America, 10 - 15 May 2015, pp.1316-1321 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/radar.2015.7131199
  • City: Virginia
  • Country: United States Of America
  • Page Numbers: pp.1316-1321
  • Keywords: Sufficient Statistics, Mutual Information, Detection, Reduced Rank Detection, Generalized Eigenvectors, OPTIMIZATION, EQUIVALENCE, CAPACITY
  • Middle East Technical University Affiliated: Yes

Abstract

The detection and estimation problems with large dimensional vectors frequently appear in the phased array radar systems equipped with, possibly, several hundreds of receiving elements. For such systems, a preprocessing stage reducing the large dimensional input to a manageable dimension is required. The present work shows that the subspace spanned by the generalized eigenvectors of signal and noise covariance matrices is the optimal subspace to this aim from several different viewpoints. Numerical results on the subspace selection for the radar target detection problem is provided to examine the performance of detectors with reduced dimensions.