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Proceedings Paper

Mining vibrometry signatures to determine target separability
Author(s): Mark R. Stevens; Daniel W. Stouch; Magnus Snorrason; Frederick Heitkamp
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Paper Abstract

Laser vibrometry sensors measure minute surface motion colinear with the sensor's line-of-sight. If the vibrometry sensor has a high enough sampling rate, an accurate estimate of the surface vibration is measured. For vehicles with running engines, an automatic target recognition algorithm can use these measurements to produce identification estimates. The level of identification possible is a function of the distinctness of the vibration signature. This signature is dependent upon many factors, such as engine type and vehicle weight. In this paper, we present results of using data mining techniques to assess the identification potential of vibrometry data. Our technique starts with unlabeled vibrometry measurements taken from a variety of vehicles. Then an unsupervised clustering algorithm is run on features extracted from this data. The final step is to analyze the produced cluters and determine if physical vehicle characteristics can be mapped onto the clusters.

Paper Details

Date Published: 16 September 2003
PDF: 8 pages
Proc. SPIE 5094, Automatic Target Recognition XIII, (16 September 2003); doi: 10.1117/12.485709
Show Author Affiliations
Mark R. Stevens, Charles River Analytics, Inc. (United States)
Daniel W. Stouch, Charles River Analytics, Inc. (United States)
Magnus Snorrason, Charles River Analytics, Inc. (United States)
Frederick Heitkamp, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 5094:
Automatic Target Recognition XIII
Firooz A. Sadjadi, Editor(s)

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