Automatic Target Recognition

These examples are illustrative of the potential of synthetic aperture imaging. However, it should be appreciated that the challenge is to extract useful information on the desired targets from such imagery. 

The problem of determining the class to which a target belongs directly relies upon the amount of information available. ATRs are systems that contain an input sub-system that accepts pattern vectors from the 

Three algorithms have been implemented in both single and multiperspective environments. In this way any bias introduced by a single algorithm should be removed. The first is the statistical Naive Bayesian Classifier, ft reduces the decision-making problem to simple calculations of feature probabilities, ft is based on Bayes theorem and calculates the posterior probability of classes conditioned on the given unknown feature 

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Herman, S. and Moulin, P.M. A particle filtering approach to passive radar tracking and automatic target recognition IEEE Aerospace Conference Proceedings, 2002. Vol.4, pp 1789-41808, 9-16 March 2002. 

Elirman, L.M. and Lanterman, A.D. A robust algorithm for automatic target recognition using passive radar Proceedings of the Thirty-Sixth Southeastern Symposium on System Theory, 2004 pp 102-106, March 14-16, 2004. 

Development of system concepts for contamination avoidance (CA). For CA applications (i.e., the existing sensors already in the field or in development), the implications of networking remote arrays—for example, automatic target recognition, fusion, data and communications formatting, and so on—are largely nonexistent. 

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