Parametric GLRT for multichannel adaptive signal detection

Kwang June Sohn, Hongbin Li, Braham Himed

Research output: Contribution to journalArticlepeer-review

80 Scopus citations

Abstract

This paper considers the problem of detecting a multichannel signal in the presence of spatially and temporally colored disturbance. A parametric generalized likelihood ratio test (GLRT) is developed by modeling the disturbance as a multichannel autoregressive (AR) process. Maximum likelihood (ML) parameter estimation underlying the parametric GLRT is examined. It is shown that the ML estimator for the alternative hypothesis is nonlinear and there exists no closed-form expression. To address this issue, an asymptotic ML (AML) estimator is presented, which yields asymptotically optimum parameter estimates at reduced complexity. The performance of the parametric GLRT is studied by considering challenging cases with limited or no training signals for parameter estimation. Such cases (especially when training is unavailable) are of great interest in detecting signals in heterogeneous, fast changing, or dense-target environments, but generally cannot be handled by most existing multichannel detectors which rely more heavily on training at an adequate level. Compared with the recently introduced parametric adaptive matched filter (PAMF) and parametric Rao detectors, the parametric GLRT achieves higher data efficiency, offering improved detection performance in general.

Original languageEnglish
Pages (from-to)5351-5360
Number of pages10
JournalIEEE Transactions on Signal Processing
Volume55
Issue number11
DOIs
StatePublished - Nov 2007

Keywords

  • Generalized likelihood ratio test (GLRT)
  • Maximum likelihood (ML) parameter estimation
  • Multichannel signal detection
  • Parametric models
  • Space-time adaptive processing (STAP)

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