DocumentCode :
768979
Title :
Canonical detection in spherically invariant noise
Author :
Conte, E. ; Di Bisceglie, M. ; Longo, Maurizio ; Lops, M.
Author_Institution :
Dipartimento di Ingegneria Elettronica, Naples Univ., Italy
Volume :
43
Issue :
38020
fYear :
1995
Firstpage :
347
Lastpage :
353
Abstract :
The paper deals with the detection of signals with unknown parameters in impulsive noise, modeled as a spherically symmetric random process. The proposed model subsumes several interesting families of noise amplitude distributions: generalized Cauchy, generalized Laplace, generalized Gaussian, contaminated normal. It also allows handling of the case of correlated noise by a whitening approach. The generalized maximum likelihood decision strategy is adopted, resulting in a canonical detector, which is independent of the amplitude distribution of the noise. A general method for performance evaluation is outlined, and a comprehensive performance analysis is carried out for the case of M-ary equal-energy orthogonal signals under several distributional assumptions for the noise. The performance is contrasted with that of the maximum likelihood receiver for completely known signals, so as to assess the loss due to the a-priori uncertainty as to the signal parameters.<>
Keywords :
Gaussian distribution; correlation methods; maximum likelihood detection; maximum likelihood estimation; noise; normal distribution; parameter estimation; performance evaluation; probability; random processes; receivers; M-ary equal-energy orthogonal signals; a-priori uncertainty; canonical detection; contaminated normal distribution; correlated noise; generalized Cauchy distribution; generalized Gaussian distribution; generalized Laplace distribution; generalized maximum likelihood decision; impulsive noise; maximum likelihood receiver; noise amplitude distributions; performance analysis; performance evaluation; signal detection; signal parameters; spherically invariant noise; spherically symmetric random process; whitening approach; Detectors; Gaussian noise; Maximum likelihood detection; Noise level; Performance analysis; Performance loss; Random processes; Signal detection; Signal processing; Uncertainty;
fLanguage :
English
Journal_Title :
Communications, IEEE Transactions on
Publisher :
ieee
ISSN :
0090-6778
Type :
jour
DOI :
10.1109/26.380053
Filename :
380053
Link To Document :
بازگشت