Scattering-Based Tomography for HRR and SAR Prediction
This paper introduces a method for predicting HRR radar signatures and SAR images by creating a parametric three-dimensional scattering model from existing measured or model-based HRR signatures and/or SAR images. The method identifies potential three-dimensional persistent scatterers and estimates their scattering patterns. The results are parametric HRR signature and SAR image reconstruction functions of range, azimuth, and elevation.
The modeling is accomplished through a scattering-based tomography technique. This technique localizes potential scatterers by using a filtered back-projection algorithm for the inverse radon transform. Once found, potential scatterers may then have their two-dimensional (azimuth and elevation) scattering patterns parameterized through the use of a truncated spherical harmonic series.
Results using the reconstructions from HRR data are presented. A M109 model is reconstructed based on HRR signatures. The model allows us to predict what the vehicle would look like from any arbitrary orientation using SAR. Finally an M548 vehicle is modeled using 26 measured HRR signatures. The model is shown to be better than the synthetic model data. Additionally we show that the new model results can be combined with the synthetic data to provide a better target model for signature matching.
Key WordsATR HRR SAR tomography radar prediction
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