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Proceedings Paper

Adaptive statistical inferential methods for detection and classification in sensor systems
Author(s): Xinjia Chen; Ernest Walker
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Paper 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.

Paper Details

Date Published: 5 May 2011
PDF: 11 pages
Proc. SPIE 8050, Signal Processing, Sensor Fusion, and Target Recognition XX, 80501K (5 May 2011); doi: 10.1117/12.883623
Show Author Affiliations
Xinjia Chen, Southern Univ. (United States)
Ernest Walker, Southern Univ. (United States)

Published in SPIE Proceedings Vol. 8050:
Signal Processing, Sensor Fusion, and Target Recognition XX
Ivan Kadar, Editor(s)

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