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

Research output: Contribution to journalArticle

35 Citations (Scopus)

Abstract

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
Volume3
Issue number8
DOIs
StatePublished - Dec 1 1996
Externally publishedYes

Fingerprint

Generalized Likelihood Ratio Test
Minimum Variance
Parameter estimation
Parameter Estimation
Statistics
Likelihood Ratio Test Statistic
Output
Data Distribution
Beamforming
Maximum Likelihood Estimate
Maximum likelihood
Test Statistic
Linearly
Robustness
Class

ASJC Scopus subject areas

  • Signal Processing
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

A note on non-Gaussian adaptive array detection and signal parameter estimation. / Richmond, Christ.

In: IEEE Signal Processing Letters, Vol. 3, No. 8, 01.12.1996, p. 251-252.

Research output: Contribution to journalArticle

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