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Proceedings Paper

Superresolution HRR ATR performance with HDVI
Author(s): Duy H. Nguyen; Gerald R. Benitz; John H. Kay; Robert H. Whiting
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Paper Abstract

A goal of super-resolution, in addition to improving probability of correct classification (Pcc) in automatic target recognition systems, is to reduce radar resource requirements in achieving a given Pcc. These studies address the MIT Lincoln Laboratory 1-D template-based ATR algorithm that was developed and tested on super-resolved high range resolution (HRR) profiles formed from synthetic aperture radar (SAR) images of targets taken from the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set. Previous studies on HRR ATR demonstrated encouraging results for recognition of stationary targets from their HRR profiles, although the low probability of correct classification dictates a large margin of improvement in Pcc is needed before the system can be operational. In this work, a super- resolution technique known as High Definition Vector Imaging (HDVI) is applied to the HRR profiles before the profiles are passed through the ATR classification. The new 1-D ATR system using super-resolved HRR demonstrates significantly improved target recognition compared to previous 1-D ATR systems that use conventional image processing techniques. This paper discusses the improvement in HRR ATR performance in terms of radar resource requirements as a result of applying HDVI.

Paper Details

Date Published: 17 August 2000
PDF: 10 pages
Proc. SPIE 4050, Automatic Target Recognition X, (17 August 2000); doi: 10.1117/12.395587
Show Author Affiliations
Duy H. Nguyen, MIT Lincoln Lab. (United States)
Gerald R. Benitz, MIT Lincoln Lab. (United States)
John H. Kay, MIT Lincoln Lab. (United States)
Robert H. Whiting, MIT Lincoln Lab. (United States)


Published in SPIE Proceedings Vol. 4050:
Automatic Target Recognition X
Firooz A. Sadjadi, Editor(s)

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