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Proceedings Paper

Characterizing detection thresholds using extreme value theory in compressive noise radar imaging
Author(s): Mahesh C. Shastry; Ram M. Narayanan; Muralidhar Rangaswamy
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Paper Abstract

An important outcome of radar signal processing is the detection of the presence or absence of target reflections at each pixel location in a radar image. In this paper, we propose a technique based on extreme value theory for characterizing target detection in the context of compressive sensing. In order to accurately characterize target detection in radar systems, we need to relate detection thresholds and probabilities of false alarm. However, when convex optimization algorithms are used for compressive radar imaging, the recovered signal may have unknown and arbitrary probability distributions. In such cases, we resort to Monte Carlo simulations to construct empirical distributions. Computationally, this approach is impractical for computing thresholds for low probabilities of false alarm. We propose to circumvent this problem by using results from extreme-value theory.

Paper Details

Date Published: 31 May 2013
PDF: 9 pages
Proc. SPIE 8717, Compressive Sensing II, 87170B (31 May 2013); doi: 10.1117/12.2016899
Show Author Affiliations
Mahesh C. Shastry, The Pennsylvania State Univ. (United States)
Ram M. Narayanan, The Pennsylvania State Univ. (United States)
Muralidhar Rangaswamy, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 8717:
Compressive Sensing II
Fauzia Ahmad, Editor(s)

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