A note on non-Gaussian adaptive array detection and signal parameter estimation

Christ D. Richmond

Research output: Contribution to journalArticlepeer-review

45 Scopus citations


Kelly's generalized likelihood ratio test (GLRT) statistic is reexamined under a broad class of data distributions known as complex multivariate elliptically contoured (MEC), which include the complex Gaussian as a special case. We show that, mathematically, Kelly's GLRT test statistic is again obtained when the data matrix is assumed MEC distributed. The maximum-likelihood (ML) estimate for the signal parameters - alias the sample-covariance-based (SCB) minimum variance distortionless response beamformer output and, in general, the SCB linearly constrained minimum variance beamformer output - is likewise shown to be the same. These results have significant robustness implications to adaptive detection/estimation/beamforming in non-Gaussian environments.

Original languageEnglish (US)
Pages (from-to)251-252
Number of pages2
JournalIEEE Signal Processing Letters
Issue number8
StatePublished - 1996
Externally publishedYes

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics


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