Neyman-Pearson detection for CSO processing

Theagenis J. Abatzoglou; John T. Reagan

doi:10.1117/12.162039

29 October 1993 Neyman-Pearson detection for CSO processing

Theagenis J. Abatzoglou, John T. Reagan

Proceedings Volume 2032, Neural and Stochastic Methods in Image and Signal Processing II; (1993) https://doi.org/10.1117/12.162039
Event: SPIE's 1993 International Symposium on Optics, Imaging, and Instrumentation, 1993, San Diego, CA, United States

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.

Citation Download Citation

Theagenis J. Abatzoglou and John T. Reagan "Neyman-Pearson detection for CSO processing", Proc. SPIE 2032, Neural and Stochastic Methods in Image and Signal Processing II, (29 October 1993); https://doi.org/10.1117/12.162039

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