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Proceedings Paper

Simple suboptimal solution to the sensor-optimized fusion-optimized Neyman-Pearson constrained decision fusion problem
Author(s): Robert J. Pawlak
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Paper Abstract

Many previous approaches to the Neyman-Pearson constrained fusion of statistically independent sensor decisions have concentrated on optimizing system performance at the sensors or at the fusion center but not both. This paper discusses a simple method for optimizing decision fusion system performance when the thresholds at the sensors and at the fusion center are allowed to vary. Two methods for optimizing system performance are investigated. The first method seeks to optimize system performance by selecting the local minimum probability of error thresholds at the sensor followed by an optimization of the fusion center weights. The second method is similar to the first, except it examines all possible combinations of the best two local minimum probability of error thresholds. Simulation results are used to show that while these two methods do not always provide an optimal solution, they are sufficiently accurate in many applications. Both methods are easily implemented and may be useful for real-time applications in which exhaustive computation of sensor operating points is not feasible, or for providing an initial solution for other more sophisticated methods.

Paper Details

Date Published: 14 June 1996
PDF: 8 pages
Proc. SPIE 2755, Signal Processing, Sensor Fusion, and Target Recognition V, (14 June 1996); doi: 10.1117/12.243163
Show Author Affiliations
Robert J. Pawlak, Naval Surface Warfare Ctr. (United States)


Published in SPIE Proceedings Vol. 2755:
Signal Processing, Sensor Fusion, and Target Recognition V
Ivan Kadar; Vibeke Libby, Editor(s)

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