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Evaluation Testbed for ATD/T Performance Prediction (ETAPP)

Award Information
Agency: Department of Defense
Branch: Army
Contract: W31P4Q-06-C-0233
Agency Tracking Number: A043-166-1338
Amount: $728,696.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: A04-166
Solicitation Number: 2004.3
Timeline
Solicitation Year: 2004
Award Year: 2006
Award Start Date (Proposal Award Date): 2006-05-17
Award End Date (Contract End Date): 2008-05-30
Small Business Information
625 Mount Auburn Street,
Cambridge, MA 02138
United States
DUNS: 115243701
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Scott Ralph
 Senior Scientist
 (617) 491-3474
 sralph@cra.com
Business Contact
 Paul Gonsalves
Title: Vice President
Phone: (617) 491-3474
Email: pgonsalves@cra.com
Research Institution
N/A
Abstract

Currently, automatic target recognition (ATR) evaluation techniques use simple models, such as quick-look models, or detailed exhaustive simulation. Simple models cannot accurately quantify performance, while the detailed simulation requires enumerating each operating condition. A need exists for ATR performance prediction based on more accurate models. We develop a predictor based on image measures quantifying the intrinsic ATR difficulty on an image. We have implemented several image measures, including: CFAR, Power Spectrum Signature, and others. We propose a two-phase approach: a learning phase, where image measures are computed for a set of test images, and the ATR performance is measured; and a performance prediction phase. The learning phase produces a mapping, valid accross various ATR algorithms, which is even applicable when no image truth is available (e.g., when evaluating denied area imagery). The testbed has plug-in capability to allow rapid evaluation of new ATR algorithms. We present a performance predictor using a trained classifier ATR that was constructed using GENIE, a tool developed at Los Alamos. We have also formulated a measure that quantifies the fidelity of synthetically generated infrared imagery fidelity. This measure describes how well a set of real imagery is covered by synthetic imagery samples.

* Information listed above is at the time of submission. *

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