Single Object Detection Using Multiple Sensors with Unknown Noise Distributions
Date of Award
Master of Science (MS)
Nageswara S. V. Rao
Call Number for Print
Special Collections LD4331.C65C53
We consider the design of an object classification system that identifies single objects using a system of sensors; each sensor outputs a random vector, according to an unknown (noise) probability distribution, in response to a sensed object. We consider a special class of systems, called the linearly separable systems, where the error-free sensor outputs corresponding to distinct objects can be mapped into disjoint intervals on real line. Given a set of sensor outputs corresponding to known objects, we show that a detection rule αemp that approaches the correct rule with a high probability can be computed. We show that the underlying computational problem can be reduced to a special case of the quadratic programming problem which can be solved in polynomial time.
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"Single Object Detection Using Multiple Sensors with Unknown Noise Distributions"
(1992). Master of Science (MS), Thesis, Computer Science, Old Dominion University, DOI: 10.25777/wbrd-4c02