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Proceedings Paper

ATR performance modeling concepts
Author(s): Timothy D. Ross; Hyatt B. Baker; Adam R. Nolan; Ryan E. McGinnis; Christopher R. Paulson
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Paper Abstract

Performance models are needed for automatic target recognition (ATR) development and use. ATRs consume sensor data and produce decisions about the scene observed. ATR performance models (APMs) on the other hand consume operating conditions (OCs) and produce probabilities about what the ATR will produce. APMs are needed for many modeling roles of many kinds of ATRs (each with different sensing modality and exploitation functionality combinations); moreover, there are different approaches to constructing the APMs. Therefore, although many APMs have been developed, there is rarely one that fits a particular need. Clarified APM concepts may allow us to recognize new uses of existing APMs and identify new APM technologies and components that better support coverage of the needed APMs. The concepts begin with thinking of ATRs as mapping OCs of the real scene (including the sensor data) to reports. An APM is then a mapping from explicit quantized OCs (represented with less resolution than the real OCs) and latent OC distributions to report distributions. The roles of APMs can be distinguished by the explicit OCs they consume. APMs used in simulations consume the true state that the ATR is attempting to report. APMs used online with the exploitation consume the sensor signal and derivatives, such as match scores. APMs used in sensor management consume neither of those, but estimate performance from other OCs. This paper will summarize the major building blocks for APMs, including knowledge sources, OC models, look-up tables, analytical and learned mappings, and tools for signal synthesis and exploitation.

Paper Details

Date Published: 14 May 2016
PDF: 20 pages
Proc. SPIE 9843, Algorithms for Synthetic Aperture Radar Imagery XXIII, 98430G (14 May 2016); doi: 10.1117/12.2229129
Show Author Affiliations
Timothy D. Ross, Matrix Research Inc. (United States)
Hyatt B. Baker, Air Force Research Lab. (United States)
Adam R. Nolan, Etegent Technologies, Ltd. (United States)
Ryan E. McGinnis, Matrix Research Inc. (United States)
Christopher R. Paulson, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 9843:
Algorithms for Synthetic Aperture Radar Imagery XXIII
Edmund Zelnio; Frederick D. Garber, Editor(s)

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