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Accession Number ADA564774
Title Joint PDF Construction for Sensor Fusion and Distributed Detection.
Publication Date Jul 2010
Media Count 7p
Personal Author D. Emge Q. Ding S. Kay
Abstract A novel method of constructing a joint probability density function (PDF) under H(sub 1), when the joint PDF under H(sub 0) is known, is developed. It has direct application in distributed detection systems. The construction is based on the exponential family, and it is shown that asymptotically the constructed PDF is optimal. The generalized likelihood ratio test (GLRT) is derived based on this method for the partially observed linear model. Interestingly, the test statistic is equivalent to the clairvoyant GLRT, which uses the true PDF under H(sub 1), even if the noise is nonGaussian.
Keywords Algorithms
Asymptotic normality
Computerized simulation
Data fusion
Detection
Distributed detection systems
Exponential family
Gaussian mixture models
Gaussian noise
Generalized likelihood ratio test
Joint probability density function
Kullback-leibler divergence
Maximum likelihood estimation
Measurement
Multisensors
Nongaussian noise
Optimization
Parameters
Probability density functions
Reference probability density function
Sensor fusion
Symposia

 
Source Agency Non Paid ADAS
NTIS Subject Category 72F - Statistical Analysis
62 - Computers, Control & Information Theory
63F - Optical Detection
Corporate Author Rhode Island Univ., Kingston. Dept. of Electrical and Computer Engineering.
Document Type Technical report
Title Note Conference paper.
NTIS Issue Number 1303
Contract Number FA8650-08-D-1303-0006 W91ZLK-08-P-1214

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