Title :
Joint PDF construction for sensor fusion and distributed detection
Author :
Kay, S. ; Quan Ding ; Emge, D.
Author_Institution :
Dept. of Electr., Comput., & Biomed. Eng., Univ. of Rhode Island, Kingston, RI, USA
Abstract :
A novel method of constructing a joint PDF under H1, when the joint PDF under H0 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 H1, even if the noise is non-Gaussian.
Keywords :
probability; sensor fusion; signal detection; distributed detection systems; generalized likelihood ratio test; joint PDF construction; partially observed linear model; probability density function; sensor fusion; Biomedical measurements; Gaussian noise; Joints; Maximum likelihood estimation; Probability density function; Vectors; Distributed detection; Gaussian mixture; data fusion; exponential family; joint PDF;
Conference_Titel :
Information Fusion (FUSION), 2010 13th Conference on
Conference_Location :
Edinburgh
Print_ISBN :
978-0-9824438-1-1
DOI :
10.1109/ICIF.2010.5711848
