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Proceedings Paper

New neural network architecture for the fusion of independent or dependent sensor decisions
Author(s): Robert J. Pawlak
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Paper Abstract

A new neural network architecture for binary hypothesis testing is discussed. The network can utilize results from sensors making independent or dependent decisions (as well as any combination of binary data). Furthermore, it employs a novel structure, incorporating a set of trainable threshold values but no trainable weight values. The threshold values are trained using a minimum probability of error criterion, and only one threshold is modified for each training sample. Simulation results are presented comparing the performance of the network with that of the optimal parametric detector for the case of independent sensor decisions. These results show that for independent data, the performance of the net approaches that of the optimal parametric detector.

Paper Details

Date Published: 10 June 1994
PDF: 11 pages
Proc. SPIE 2232, Signal Processing, Sensor Fusion, and Target Recognition III, (10 June 1994); doi: 10.1117/12.177741
Show Author Affiliations
Robert J. Pawlak, Naval Surface Warfare Ctr. (United States)


Published in SPIE Proceedings Vol. 2232:
Signal Processing, Sensor Fusion, and Target Recognition III
Ivan Kadar; Vibeke Libby, Editor(s)

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