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Simple local partition rules in multi-bit decision fusion

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2 Author(s)
Kam, Moshe ; Dept. of Electr. & Comput. Eng., Drexel Univ., Philadelphia, PA, USA ; Xiaoxun Zhu

A parallel decision fusion system is studied where local detectors (LDs) collect information about a binary hypothesis, and transmit multi-bit intermediate decisions to a data fusion center (DFC). The DFC compresses the local decisions into a final binary decision. The objective function is the Bayesian risk. Equations for the optimal decision rules for the LDs and the DFC have been derived by Lee-Chao (1989), but the computational complexity of solving them is formidable. To address this difficulty, we propose several suboptimal LD-design schemes. For each one we design a DFC, which is optimally conditioned on the fixed LD rules. We calculate the exact performance of each scheme, thus providing a means for selection of the most appropriate one under given observation conditions. We demonstrate performance for two important binary decision tasks: discrimination between two Gaussian hypotheses of equal variances and different means; and discrimination between two Gaussian hypotheses of equal means and different variances

Published in:

Multisensor Fusion and Integration for Intelligent Systems, 1994. IEEE International Conference on MFI '94.

Date of Conference:

2-5 Oct 1994