Applying manifold learning to vehicle classification using vibrometry signatures

Scott Kangas; Olga Mendoza-Schrock; Andrew Freeman

doi:10.1117/12.2018904

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28 May 2013 Applying manifold learning to vehicle classification using vibrometry signatures

Scott Kangas, Olga Mendoza-Schrock, Andrew Freeman

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Proceedings Volume 8751, Machine Intelligence and Bio-inspired Computation: Theory and Applications VII; 87510G (2013) https://doi.org/10.1117/12.2018904
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

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.

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Scott Kangas, Olga Mendoza-Schrock, and Andrew Freeman "Applying manifold learning to vehicle classification using vibrometry signatures", Proc. SPIE 8751, Machine Intelligence and Bio-inspired Computation: Theory and Applications VII, 87510G (28 May 2013); https://doi.org/10.1117/12.2018904

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