  Radio frequency (RF) sensors are used alongside other sensing modalities to
provide rich representations of the world. Given the high variability of
complex-valued target responses, RF systems are susceptible to attacks masking
true target characteristics from accurate identification. In this work, we
evaluate different techniques for building robust classification architectures
exploiting learned physical structure in received synthetic aperture radar
signals of simulated 3D targets.
