Matched subspace detectors Scharf, Louis L. ; Friedlander, Benjamin "This work was supported by the Office of Naval Research, Mathematics Division, Statistics and Probability Branch, under Contracts N00014-89-J-1070 and N00014-91-J-1602, and by the National Science Foundation under Grant MIP-90-17221." In this paper 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. Colorado State University. Libraries 1994 text ; image application/pdf ECElls00006.pdf FACFECEN100395ARTI eng c1994 IEEE
Matched subspace detectors
Scharf, Louis L. ; Friedlander, Benjamin
"This work was supported by the Office of Naval Research, Mathematics Division, Statistics and Probability Branch, under Contracts N00014-89-J-1070 and N00014-91-J-1602, and by the National Science Foundation under Grant MIP-90-17221."
In this paper 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.
Colorado State University. Libraries
1994
text ; image
application/pdf
ECElls00006.pdf
FACFECEN100395ARTI
eng
c1994 IEEE