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Proceedings Paper

Applying manifold learning to vehicle classification using vibrometry signatures
Author(s): Scott Kangas; Olga Mendoza-Schrock; Andrew Freeman
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Paper Abstract

Understanding and organizing data is the first step toward exploiting laser vibrometry sensor phenomenology for target classification. A fundamental challenge in robust vehicle classification using vibrometry signature data is the determination of salient signal features and the fusion of appropriate measurements. . A particular technique, Diffusion Maps, has demonstrated the potential to extract intuitively meaningful features [1]. We want to develop an understanding of this technique by validating existing results using vibrometry data. This paper briefly describes the Diffusion Map technique, its application to dimension reduction of vibrometry data, and describes interesting problems to be further explored.

Paper Details

Date Published: 28 May 2013
PDF: 9 pages
Proc. SPIE 8751, Machine Intelligence and Bio-inspired Computation: Theory and Applications VII, 87510G (28 May 2013); doi: 10.1117/12.2018904
Show Author Affiliations
Scott Kangas, Air Force Research Lab. (United States)
Olga Mendoza-Schrock, Air Force Research Lab. (United States)
Andrew Freeman, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 8751:
Machine Intelligence and Bio-inspired Computation: Theory and Applications VII
Misty Blowers; Olga Mendoza-Schrock, Editor(s)

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