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Proceedings Paper

Model matching for SAR ATR based on probabilistic distance transforms
Author(s): David M. Doria
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Paper Abstract

In this paper we present an analysis of distance transform methods of matching object models to SAR data. We show that by properly defining the distance function, the likelihood of each observed SAR feature data point given the model is given as a function of position. This allows calculation of a likelihood of observing a set of data features, given a model and its associated pose and other parameters. The issue of normalization resulting from the non-correspondence based distance transform method is discussed. When prior densities of the model features are available, maximum a- posteriori results are obtainable. This method allows the use of priors of models and individual features, along with the geometric probability densities associated with the feature prediction and measurement processes, to be incorporated within a fast correlation-type distance transform matching module. The method also potentially allows exploitation of persistent scatterers over a limited range of SAR model-to-target imaging parameters.

Paper Details

Date Published: 10 June 1997
PDF: 8 pages
Proc. SPIE 3066, Radar Sensor Technology II, (10 June 1997); doi: 10.1117/12.276093
Show Author Affiliations
David M. Doria, Hughes Aircraft Co. (United States)


Published in SPIE Proceedings Vol. 3066:
Radar Sensor Technology II
Robert Trebits; James L. Kurtz, Editor(s)

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