Matched subspace detectors

Author

Scharf, Louis L. ; Friedlander, Benjamin

Author_Institution

Dept. of Electr. & Comput. Eng., Colorado Univ., Boulder, CO, USA

Volume

42

Issue

8

fYear

1994

fDate

8/1/1994 12:00:00 AM

Firstpage

2146

Lastpage

2157

Abstract

We formulate a general class of problems for detecting subspace signals in subspace interference and broadband noise. We derive the generalized likelihood ratio (GLR) for each problem in the class. We then establish the invariances for the GLR and argue that these are the natural invariances for the problem. In each case, the GLR is a maximal invariant statistic, and the distribution of the maximal invariant statistic is monotone. This means that the GLR test (GLRT) is the uniformly most powerful invariant detector. We illustrate the utility of this finding by solving a number of problems for detecting subspace signals in subspace interference and broadband noise. In each case we give the distribution for the detector and compute performance curves

Keywords

electromagnetic interference; filtering and prediction theory; linear algebra; matched filters; noise; signal detection; GLR; broadband noise; generalized likelihood ratio; invariances; invariant detector; matched subspace detectors; maximal invariant statistic; monotone distribution; performance curves; subspace interference; subspace signal detection; Detectors; Distributed computing; Interference; Matched filters; Signal detection; Signal processing; Sonar detection; Statistical distributions; Statistics; Testing;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.301849

Filename

301849