On the generalized likelihood ratio test for a class of nonlinear detection problems

Author

Porat, Boaz ; Friedlander, Benjamin

Author_Institution

Dept. of Electr. Eng., Technion-Israel Inst. of Technol., Haifa, Israel

Volume

41

Issue

11

fYear

1993

fDate

11/1/1993 12:00:00 AM

Firstpage

3186

Lastpage

3190

Abstract

The authors examine the generalized likelihood ratio test (GLRT) for a certain class of detection problems. This class is characterized by a model which is linear in some parameters and nonlinear in others. They show that the classical asymptotic analysis of the GLRT fails for this class, and demonstrate the existence of ill-behaved cases in this class. They then propose a modification of the GLRT. The main advantage of this modification is that its probability of false-alarm is easily computable, thus facilitating the choice of threshold according to the Neyman-Pearson criterion. Performance analysis of the modified GLRT is provided and supported by simulations

Keywords

maximum likelihood estimation; probability; signal detection; Neyman-Pearson criterion; asymptotic analysis; false-alarm probability; generalized likelihood ratio test; modified GLRT; nonlinear detection problems; performance analysis; simulations; threshold; Analytical models; Computational modeling; Detectors; Failure analysis; Gaussian noise; Maximum likelihood detection; Maximum likelihood estimation; Performance analysis; Signal detection; Testing;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.257252

Filename

257252