Abstract
The problem of distributed detection involving N sensors is considered. The configuration of sensors is serial in the sense that the (j-1)th sensor passes its decision to the jth sensor and that the jth sensor decides using the decision it receives and its own observation. When each sensor employs the Neyman-Pearson test, the probability of detection is maximized for a given probability of false alarm, at the Nth stage. With two sensors the serial scheme is better than the parallel fusion scheme analyzed in the literature. For certain distributions of observations, the serial scheme performs better for all N. Numerical examples illustrate the global optimization by the selection of operating thresholds at the sensors.
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Comments
Viswanathan, R., Thomopoulos, S.C.A., & Tumuluri, R. (1987). Optimal serial distributed decision fusion. 26th IEEE Conference on Decision and Control, 1987, v.26, part 1, 1848 - 1849. DOI: 10.1109/CDC.1987.272831 ©1987 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.