SAMPLING DESIGN FOR GAUSSIAN DETECTION PROBLEMS

In this paper, we propose an approach for the design of sampling schem es for Gaussian hypothesis testing problems, Our approach for this des ign is based on the class of Ali-Silvey distance measures, Closed form s for the Bhattacharyya distance, the I-divergence, the J-divergence, and the Chernoff distance between the class conditional densities are obtained for the sampling design problem in the strong signal case, A new member of the class of Ali-Silvey distance measures that is suitab le for the detection problem in the weak signal case is also derived. Sampling schemes are determined to maximize those four distance measur es as well as the new distance measure for the strong signal case and the weak signal case, respectively, Detection performance of our sampl ing schemes is compared with those of various other sampling schemes b y means of numerical examples, Comparisons show that the sampling desi gn based on Ali-Silvey distance measures result in superior performanc e.