Adaptive statistical inferential methods for detection and classification in sensor systems

Xinjia Chen; Ernest Walker

doi:10.1117/12.883623

5 May 2011 Adaptive statistical inferential methods for detection and classification in sensor systems

Xinjia Chen, Ernest Walker

Proceedings Volume 8050, Signal Processing, Sensor Fusion, and Target Recognition XX; 80501K (2011) https://doi.org/10.1117/12.883623
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

Abstract

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.

Citation Download Citation

Xinjia Chen and Ernest Walker "Adaptive statistical inferential methods for detection and classification in sensor systems", Proc. SPIE 8050, Signal Processing, Sensor Fusion, and Target Recognition XX, 80501K (5 May 2011); https://doi.org/10.1117/12.883623

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