DocumentCode :
1162102
Title :
Optimum distributed detection of weak signals in dependent sensors
Author :
Blum, Rick S. ; Kassam, Saleem A.
Author_Institution :
Dept. of Comput. Sci. & Electr. Eng., Lehigh Univ., Bethlehem, PA, USA
Volume :
38
Issue :
3
fYear :
1992
fDate :
5/1/1992 12:00:00 AM
Firstpage :
1066
Lastpage :
1079
Abstract :
Locally optimum (LO) distributed detection is considered for observations that are dependent from sensor to sensor. The necessary conditions are presented for LO distributed sensor detector designs. and a locally optimum fusion rule for an N-sensor parallel distributed detection system with dependent sensor observations is given. Specific solutions are obtained for a random signal additive noise detection problem with two sensors. These solutions indicate that the LO sensor detector nonlinearities, in general, contain a term proportional to f´/f, where f is the noise probability density function (pdf). For some non-Gaussian pdf´s, the new term is significant and causes the LO sensor detector nonlinearities to be nonsymmetric even for symmetric pdfs. LO solutions are presented for finite sample sizes, and the solutions for the asymptotic case are discussed. These results are extended to yield the form of the solutions for the N-sensor LO random signal distributed detection problem that generalize the two-sensor results
Keywords :
random noise; signal detection; N-sensor parallel distributed detection system; asymptotic case; dependent sensors; detector nonlinearities; finite sample sizes; locally optimum distributed detection; locally optimum fusion rule; noise probability density function; random signal additive noise detection problem; signal detection; weak signals; Additive noise; Costs; Detectors; Equations; Gaussian noise; Probability density function; Sensor fusion; Sensor systems; Signal design; Signal detection;
fLanguage :
English
Journal_Title :
Information Theory, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9448
Type :
jour
DOI :
10.1109/18.135646
Filename :
135646
Link To Document :
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