Adaptive statistical inferential methods for detection and classification in sensor systems
Xinjia Chen, Ernest Walker · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2011
In this paper, we investigate the multiple hypothesis problems of target detection and tracking in sensor systems. In many practical situations, the observational data may be expensive to acquire and the speed of decision can be affected by unnecessary amount of observational data. Motivated by the importance of accuracy and efficiency of sensor systems, we propose novel adaptive statistical inferential methods to reduce the amount of required observational data while achieving acceptable level of accuracy. Toward this goal, we propose adaptive methods in the general framework of testing multiple hypotheses for the detection and classification problems. The feasibility and optimality of the methods have been established.