  Multiple-input multiple-output (MIMO) radar is one of the leading depth
sensing modalities. However, the usage of multiple receive channels lead to
relative high costs and prevent the penetration of MIMOs in many areas such as
the automotive industry. Over the last years, few studies concentrated on
designing reduced measurement schemes and image reconstruction schemes for MIMO
radars, however these problems have been so far addressed separately. On the
other hand, recent works in optical computational imaging have demonstrated
growing success of simultaneous learning-based design of the acquisition and
reconstruction schemes, manifesting significant improvement in the
reconstruction quality. Inspired by these successes, in this work, we propose
to learn MIMO acquisition parameters in the form of receive (Rx) antenna
elements locations jointly with an image neural-network based reconstruction.
To this end, we propose an algorithm for training the combined
acquisition-reconstruction pipeline end-to-end in a differentiable way. We
demonstrate the significance of using our learned acquisition parameters with
and without the neural-network reconstruction.
