Adaptive subspace detectors

Author

Kraut, Shawn ; Scharf, Louis L. ; McWhorter, L. Todd

Author_Institution

Dept. of Electr. & Comput. Eng., Duke Univ., Durham, NC, USA

Volume

49

Issue

1

fYear

2001

fDate

1/1/2001 12:00:00 AM

Firstpage

1

Lastpage

16

Abstract

We use the theory of generalized likelihood ratio tests (GLRTs) to adapt the matched subspace detectors (MSDs) of Scharf (1991) and of Scharf and Frielander (1994) to unknown noise covariance matrices. In so doing, we produce adaptive MSDs that may be applied to signal detection for radar, sonar, and data communication. We call the resulting detectors adaptive subspace detectors (ASDs). These include Kelly´s (1987) GLRT and the adaptive cosine estimator (ACE) of Kaurt and Scharh (see ibid., vol.47, p.2538-41, 1999) and of Scharf and McWhorter (see Proc. 30th Asilomar Conf. Signals, Syst., Comput., Pacific Grove, CA, 1996) for scenarios in which the scaling of the test data may deviate from that of the training data. We then present a unified analysis of the statistical behavior of the entire class of ASDs, obtaining statistically identical decompositions in which each ASD is simply decomposed into the nonadaptive matched filter, the nonadaptive cosine or t-statistic, and three other statistically independent random variables that account for the performance-degrading effects of limited training data

Keywords

adaptive estimation; adaptive signal detection; covariance matrices; filtering theory; matched filters; maximum likelihood estimation; noise; radar detection; sonar detection; statistical analysis; CFAR MSD; GLRT; MLE; adaptive cosine estimator; adaptive matched subspace detectors; adaptive subspace detectors; data communication; generalized likelihood ratio tests; maximum likelihood estimation; noise covariance matrices; nonadaptive cosine; nonadaptive matched filter; radar; signal detection; sonar; statistical behavior; statistically identical decompositions; statistically independent random variables; t-statistic; test data scaling; training data; Covariance matrix; Detectors; Radar detection; Signal detection; Signal to noise ratio; Sonar applications; Sonar detection; Testing; Training data; Variable speed drives;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.890324

Filename

890324