Title :
A note on non-Gaussian adaptive array detection and signal parameter estimation
Author :
Richmond, Christ D.
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., MIT, Cambridge, MA, USA
Abstract :
Kelly´s (1986) 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 to be 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 for adaptive detection/estimation/beamforming in non-Gaussian environments.
Keywords :
Gaussian distribution; Gaussian processes; adaptive estimation; adaptive signal detection; array signal processing; covariance analysis; matrix algebra; maximum likelihood estimation; signal sampling; GLRT statistic; MLE; adaptive beamforming; adaptive detection; adaptive estimation; beamformer output; complex Gaussian distribution; data distributions; data matrix; generalized likelihood ratio test; linearly constrained minimum variance; maximum-likelihood estimate; minimum variance distortionless response; multivariate elliptically contoured distribution; nonGaussian adaptive array detection; nonGaussian environments; robustness; sample covariance; signal parameter estimation; Adaptive arrays; Adaptive signal detection; Distortion; Maximum likelihood detection; Maximum likelihood estimation; Robustness; Signal detection; Statistical analysis; Statistical distributions; Testing;
Journal_Title :
Signal Processing Letters, IEEE
