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Proceedings Paper

Aided target recognition using hyperdimensional manifolds
Author(s): Kenny Chen; Robert Stanfill; Abhijit Mahalanobis
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Paper Abstract

We explore the use of hyperdimensional manifolds on Automatic Target Recognition (ATR) using Synthetic Aperture Radar (SAR) imagery. Data that occupies within a hyperdimensional space can be exploited using measures constrained along the inherent structure (manifold). When compared to multiple manifolds representing different classes. associations can be made by utilizing these constrained measures and data clusters to assign the closest class. We also explore the use of sparsely estimated manifolds (limited training data) and its impact on ATR results on SAR imagery.

Paper Details

Date Published: 22 May 2015
PDF: 7 pages
Proc. SPIE 9476, Automatic Target Recognition XXV, 94760E (22 May 2015); doi: 10.1117/12.2180942
Show Author Affiliations
Kenny Chen, Lockheed Martin Missiles and Fire Control (United States)
Robert Stanfill, Lockheed Martin Missiles and Fire Control (United States)
Abhijit Mahalanobis, Lockheed Martin Missiles and Fire Control (United States)


Published in SPIE Proceedings Vol. 9476:
Automatic Target Recognition XXV
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

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