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Proceedings Paper

Detection in networked radar
Author(s): Kaitlyn Beaudet; Lauren Crider; Douglas Cochran
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Paper Abstract

The potential applicability of multiple-channel coherence estimation in situations where one channel contains a noise-free signal replica (as in active radar) or a high-SNR reference signal (as in passive coherent radar) has been proposed in recent work. Invariance of the distribution of M-channel coherence estimate statistics, including recently derived variants optimized for detection of signals having known rank, to the presence of a strong signal on one channel provided all channels are independent and the other M 􀀀 1 channels contain only noise enables the desired use of these statistics without altering detection thresholds designed to provide desired false-alarm probabilities. Traditionally, multiple-channel detection using coherence estimates has assumed that time series data from all channels are aggregated at a fusion center. Mitigation of this requirement to demand global aggregation of only scalar statistics that can be computed locally by sharing of data between pairs of nodes has been explored, and the use of maximum-entropy methods to provide surrogate statistics for pairs of nodes that are not in direct communication within a network has been proposed for traditional passive detection problems. This paper examines the applicability of this idea in the presence of a strong reference channel with particular attention to ascertaining relationships between network topology and detection performance.

Paper Details

Date Published: 23 May 2013
PDF: 9 pages
Proc. SPIE 8746, Algorithms for Synthetic Aperture Radar Imagery XX, 87460V (23 May 2013); doi: 10.1117/12.2020670
Show Author Affiliations
Kaitlyn Beaudet, Arizona State Univ. (United States)
Lauren Crider, Arizona State Univ. (United States)
Douglas Cochran, Arizona State Univ. (United States)

Published in SPIE Proceedings Vol. 8746:
Algorithms for Synthetic Aperture Radar Imagery XX
Edmund Zelnio; Frederick D. Garber, Editor(s)

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