Single Object Detection Using Multiple Sensors with Unknown Noise Distributions

Date of Award

Spring 5-1992

Document Type


Degree Name

Master of Science (MS)


Computer Science

Committee Director

Nageswara S. V. Rao

Committee Member

Ravi Mukkamala

Committee Member

Larry Wilson

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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