ATR using HRR-signatures have recently gained lot of attention. A number of classification methods have been proposed using different target descriptions. The traditionally used classifier utilizing mean square error between magnitude only range profiles and templates suffers from problems with interfering scatterers. Several attempts to improve the MSE classifier both during the template formation process and in the matching have been made.We have recently presented a method that matches complex HRR signatures to target descriptions that use scattering centers. This method handle the unknown phases of the centers and thus overcomes the problem of interference between scatterers. In this paper we compare our method with a number of other methods that uses magnitude only range profiles. Those includes Mean-templates, Eigen-templates and the Specular and Diffuse scattering models.

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The problem of interfering scatters in high range resolution (HRR) radar data is addressed in this paper. We derive, using a scattering center representation of a target, classifiers that can handle the unknown phases of the centers. We also show how to incorporate uncertainties in the magnitudes and positions of the scattering centers. The automatic target recognition (ATR) problem is discussed in a Bayesian setting, and we show how the uncertainties can be handled by such scattering center classifiers. Monte Carlo simulations are used to evaluate the performance and robustness of the classifiers for simple test cases, and data from electromagnetic prediction codes are used to illustrate the behavior on real targets.

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