Using deep learning to estimate linear structure orientation in polarimetric radar data

We present experiments to explore the use of deep neural network classification models for estimating the orientation of objects with linear structures from polarimetric radar data. We derive all radar data from two physical model aircraft and their corresponding computerized surface models. We make extensive use of synthetic pre- diction to help fully span the large parameter space as is consistent with best practice. Synthetic predictions are based upon a linear quad-polarized (H: horizontal, V: vertical) Ka-band stepped frequency measurement inverse synthetic aperture radar (ISAR) turntable system located inside the Air Force Research Laboratory (AFRL) Sensor Directorate's Indoor Range. The use of multiple polarimetric channels in a deep learning classification framework are shown to significantly help estimate orientation when the co-polarization channels significantly differ from each other. Future research directions are discussed.