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Proceedings Paper

Neyman-Pearson detection for CSO processing
Author(s): Theagenis J. Abatzoglou; John T. Reagan
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Paper Abstract

This paper expands on previous research efforts for superresolving unresolved closely-space objects (CSO) present in IR focal plane data via model-based signal processing techniques. It has been shown that a model-based maximum likelihood estimation technique attains the Cramer-Rao theoretical lower bound on the source position and intensity and it is used for resolving unresolved targets. Here, we present a Neyman-Pearson log-likelihood ratio receiver structure for detecting the presence of a single unresolved target (non-CSO) versus the presence of two CSOs. We derive analytical expressions for the receiver operating characteristic (ROC) curves for the proposed receiver structure. For a given false alarm rate (i.e. declaring the presence of a two-source CSO scenario when a single-source non-CSO is present), the Neyman-Pearson receiver maximizes the probability of detection. With simulated two-source CSO data, we present a partial verification of the ROC curves.

Paper Details

Date Published: 29 October 1993
PDF: 9 pages
Proc. SPIE 2032, Neural and Stochastic Methods in Image and Signal Processing II, (29 October 1993); doi: 10.1117/12.162039
Show Author Affiliations
Theagenis J. Abatzoglou, Lockheed Palo Alto Research Lab. (United States)
John T. Reagan, Lockheed Palo Alto Research Lab. (United States)

Published in SPIE Proceedings Vol. 2032:
Neural and Stochastic Methods in Image and Signal Processing II
Su-Shing Chen, Editor(s)

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