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13 August 1999 Model-based HRR radar ATR: combining synthetic models with limited measured observations
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Abstract
This paper addresses the use of synthetic data for air-to-air High Range Resolution (HRR) radar. Target radar models are used to generate synthetic HRR signatures in order to attempt to classify targets when there is limited real-life measured data. Target models are made up of a finite set of reflective patches. Modeling these targets is often difficult and frequently produces synthetic signatures which are not sufficiently close to measured data. We describe two approaches to improve classification of targets given limited measured HRR radar profiles and lower fidelity synthetic (model-based) profiles. The first method explores the possibility of improving model fidelity given measured HRR data. Specifically we search for material coating reflectance adjustments which consistently improve the synthetic predictions. The second approach attempts to predict missing measured data from available measured data based on global properties of the synthetic data. This is accomplished by using splines to interpolate and extrapolate between measured data and based on the global features in the synthetic data. The second method has the advantage that the model need only show global trends in the profiles over various viewing angles to aid in profile prediction. We also present algorithms for the alignment and normalization of measured data to support the above algorithms. Our results show that model corrections made by our algorithm demonstrate interpolative and extrapolative properties over regions where no measured data are available.
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Bradley S. Denney, Sam N. Richman, and Rui J. P. de Figueiredo "Model-based HRR radar ATR: combining synthetic models with limited measured observations", Proc. SPIE 3721, Algorithms for Synthetic Aperture Radar Imagery VI, (13 August 1999); doi: 10.1117/12.357660; https://doi.org/10.1117/12.357660
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