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Proceedings Paper

Characterizing sensor performance in statistically-represented signal propagation environments
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Paper Abstract

Decision support tools frequently employ deterministic, physics-based models to predict environmental effects on sensor system performance. As the environment is typically poorly characterized, we seek to use statistical representations of the environment to better understand the impact of imperfect knowledge on sensor performance estimation. We have conducted numerical experiments to simulate the effects of an uncertain environment on signal propagation and detection. These experiments utilize newly published software routines for performing algebraic manipulations of statistically represented parameters in a simple sensor performance model. Variations in the source-environment-sensor chain have an impact on the final probability of detection, and we discuss the relative sensitivity of the detection results to two different statistical representations of environmental variability.

Paper Details

Date Published:
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Proc. SPIE 9842, Signal Processing, Sensor/Information Fusion, and Target Recognition XXV, 98421H; doi: 10.1117/12.2224092
Show Author Affiliations
Daniel J. Breton, U.S. Army Engineer Research and Development Ctr. (United States)
D. Keith Wilson, U.S. Army Engineer Research and Development Ctr. (United States)


Published in SPIE Proceedings Vol. 9842:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXV
Ivan Kadar, Editor(s)

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