• DocumentCode
    1743225
  • Title

    Application of maximal invariance to the ACE detection problem

  • Author

    Kraut, Shawn ; Krolik, Jeffrey

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Duke Univ., Durham, NC, USA
  • Volume
    1
  • fYear
    2000
  • fDate
    Oct. 29 2000-Nov. 1 2000
  • Firstpage
    417
  • Abstract
    Three simple adaptive detection statistics frequently appearing in the radar detection literature, which employ a sample covariance estimate for clutter suppression, are Kelly´s GLRT (generalized likelihood ratio test): the adaptive matched filter (AMF) and the adaptive cosine estimator (ACE), which is also a GLRT for the problem where the test-data power level is unconstrained relative to the training data. Bose and Steinhardt (1995, 1996) found a two-dimensional maximal invariant statistic for the adaptive detection problem for which Kelly´s statistic is a GLRT, a one-to-one function of the Kelly GLRT and the AMF. We extend this maximal-invariant framework to the adaptive detection problem for which ACE is a GLRT showing that ACE is a one-dimensional maximal invariant.
  • Keywords
    adaptive estimation; adaptive filters; adaptive signal detection; covariance analysis; interference suppression; matched filters; radar clutter; radar detection; statistical analysis; ACE detection problem; AMF; Kelly´s GLRT; adaptive cosine estimator; adaptive detection problem; adaptive detection statistics; adaptive matched filter; clutter suppression; covariance estimate; generalized likelihood ratio test; maximal invariance; maximal-invariant framework; one-dimensional maximal invariant; radar detection; test-data power level; two-dimensional maximal invariant statistic; Application software; Clutter; Detectors; Maximum likelihood estimation; Narrowband; Radar detection; Statistical analysis; Statistics; System testing; Training data;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signals, Systems and Computers, 2000. Conference Record of the Thirty-Fourth Asilomar Conference on
  • Conference_Location
    Pacific Grove, CA, USA
  • ISSN
    1058-6393
  • Print_ISBN
    0-7803-6514-3
  • Type

    conf

  • DOI
    10.1109/ACSSC.2000.910989
  • Filename
    910989