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Proceedings Paper

Adaptive algorithm for distributed signal detection system design
Author(s): Matthew C. Deans; Rick S. Blum
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Paper Abstract

Combining signal detection decisions from multiple sensors is useful in some practical communications, radar, and sonar applications. The optimum schemes for generating and combining the detector decisions have been studied for cases with independent observations from sensor to sensor. Designing schemes for cases with dependent observations from sensor to sensor is a much more difficult problem and to date very little progress has been made. Design approaches which have been suggested for these cases are quite complicated. Here a simple adaptive design approach is outlined for the important and difficult task of detecting a weak random signal in additive, possibly non-Gaussian noise. The approach is based on considering sensor decision rules and fusion rules which contain some unknown parameters. These rules have previously been shown to be optimum for cases with a larger number of observations. These previous results also show that the best parameters minimize the mean square error fit to the best centralized signal detection scheme. Based on these ideas a gradient descent algorithm is proposed for learning the best parameters. Results of the training are compared to known results for multisensor detection schemes.

Paper Details

Date Published: 15 September 1995
PDF: 10 pages
Proc. SPIE 2589, Sensor Fusion and Networked Robotics VIII, (15 September 1995); doi: 10.1117/12.220963
Show Author Affiliations
Matthew C. Deans, Lehigh Univ. (United States)
Rick S. Blum, Lehigh Univ. (United States)


Published in SPIE Proceedings Vol. 2589:
Sensor Fusion and Networked Robotics VIII
Paul S. Schenker; Gerard T. McKee, Editor(s)

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