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Proceedings Paper

Statistical models for the classification of vehicles in MMW imagery
Author(s): William Denton; Ralph Jackson; Catherine Lawlor; Adrian Britton; Andrew R. Webb
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Paper Abstract

In this paper we exploit high resolution millimeter wave radar ISAR imagery to develop a vehicle classification algorithm, which is robust to orientation and position of the vehicle in the scene. A template based approach is presented and the effect of a number of methods of creating templates investigated. To incorporate the effect of uncertainty in vehicle position and orientation, an approach based on mixture models is developed. The specification of the model is discussed and various approaches for determining the parameters of the model have been assessed. Preliminary results using mixture models to model vehicle signatures and uncertainties in position and orientation are presented. The models and techniques reported here provide a robust approach for general radar classification problems that incorporates uncertainty in a principled manner and improves generalization.

Paper Details

Date Published: 24 August 1999
PDF: 10 pages
Proc. SPIE 3718, Automatic Target Recognition IX, (24 August 1999); doi: 10.1117/12.359955
Show Author Affiliations
William Denton, Defence Evaluation and Research Agency Malvern (United Kingdom)
Ralph Jackson, Defence Evaluation and Research Agency Malvern (United Kingdom)
Catherine Lawlor, Defence Evaluation and Research Agency Malvern (United Kingdom)
Adrian Britton, Defence Evaluation and Research Agency Malvern (United Kingdom)
Andrew R. Webb, Defence Evaluation and Research Agency Malvern (United Kingdom)


Published in SPIE Proceedings Vol. 3718:
Automatic Target Recognition IX
Firooz A. Sadjadi, Editor(s)

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