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Proceedings Paper

Segregation of tracked and wheeled ground vehicle mobility mechanisms through in-situ adaptation of seismic features
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Paper Abstract

The ability to perform generalized ground vehicle classification by unattended ground sensors (UGS) is an important facet of data analysis performed by modern unattended sensor systems. Large variation in seismic signature propagation from one location to another renders exploiting seismic measurements to classify vehicles a significant challenge. This paper presents the results of using an adaptive methodology to distinguish between tracked and wheeled ground vehicle mobility mechanisms. The methodology is a passive in-situ learning process that does not rely upon an explicit calibration process but does require an estimated range to the target. Furthermore, the benefits of the seismic feature adaptation are realized with a sparse information set. There exist scenarios in which the adaptation fails to provide information when implemented as an independent process. These situations, however, may be mitigated by sharing information with other classification algorithms. Once properly initialized, the in-situ adaptation process correctly categorizes over 95% of ground vehicles.

Paper Details

Date Published: 16 April 2008
PDF: 8 pages
Proc. SPIE 6963, Unattended Ground, Sea, and Air Sensor Technologies and Applications X, 69630X (16 April 2008); doi: 10.1117/12.784435
Show Author Affiliations
Christopher G. Park, Textron Systems Corp. (United States)
James Fitzgerald, Textron Systems Corp. (United States)
Dennis Power, Textron Systems Corp. (United States)

Published in SPIE Proceedings Vol. 6963:
Unattended Ground, Sea, and Air Sensor Technologies and Applications X
Edward M. Carapezza, Editor(s)

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