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We present a framework for simulating realistic inverse synthetic apertureradar images of automotive targets at millimeter wave frequencies. The modelincorporates radar scattering phenomenology of commonly found vehicles alongwith range-Doppler based clutter and receiver noise. These images provideinsights into the physical dimensions of the target, the number of wheels andthe trajectory undertaken by the target. The model is experimentally validatedwith measurement data gathered from an automotive radar. The images from thesimulation database are subsequently classified using both traditional machinelearning techniques as well as deep neural networks based on transfer learning.We show that the ISAR images offer a classification accuracy above 90% and arerobust to both noise and clutter.

Classification Of Automotive Targets Using Inverse Synthetic Aperture Radar Images