M. C. Deans, R. S. Blum, An Adaptive Algorithm for Distributed Detection System Design, Sensor Fusion and Networked Robotics VII, Proceedings of the SPIE 2589, pp 172-179 (1995).

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 the 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 large 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.

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