Joint optimization of system design and reconstruction in MIMO radar imaging

TL;DR

This paper introduces a joint learning approach for optimizing MIMO radar system design and image reconstruction, significantly improving image quality while reducing hardware costs through end-to-end training of acquisition and reconstruction schemes.

Contribution

It presents a novel end-to-end differentiable framework for jointly optimizing MIMO radar antenna placement and neural network-based image reconstruction, inspired by optical computational imaging advances.

Findings

Learned antenna configurations improve reconstruction quality.

Joint optimization reduces hardware complexity.

End-to-end training enhances overall system performance.

Abstract

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.