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Proceedings Paper

Kernel-based feature extraction and its application on HRR signatures
Author(s): Honglin Li; Yi Zhao; Junshui Ma; Stanley C. Ahalt
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Paper Abstract

Kernel-based Feature Extraction (KFE) is an emerging nonlinear discriminant feature extraction technique. In many classification scenarios using KFE allows the dimensionality of raw data to be reduced while class separability is preserved or even improved. KFE offers better performance than alternative linear algorithms because it employs nonlinear discriminating information among the classes. In this paper, we explore the potential application of KFE to radar signatures, as might be used for Automatic Target Recognition (ATR). Radar signatures can be problematic for many traditional ATR algorithms because of their unique characteristics. For example, some unprocessed radar signatures are high dimensional, linearly inseparable, and extremely sensitive to aspect changes. Applying KFE on High Range Resolution (HRR) radar signatures, we observe that KFE is quite effective on HRR data in terms of preserving/improving separability and reducing the dimensionality of the original data. Furthermore, our experiments indicate the number of extracted features that are needed for HRR radar signatures.

Paper Details

Date Published: 25 July 2002
PDF: 8 pages
Proc. SPIE 4726, Automatic Target Recognition XII, (25 July 2002); doi: 10.1117/12.477030
Show Author Affiliations
Honglin Li, The Ohio State Univ. (United States)
Yi Zhao, The Ohio State Univ. (United States)
Junshui Ma, The Ohio State Univ. (United States)
Stanley C. Ahalt, The Ohio State Univ. (United States)

Published in SPIE Proceedings Vol. 4726:
Automatic Target Recognition XII
Firooz A. Sadjadi, Editor(s)

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