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