Abstract:

We propose a deep-unfolded, multiple-input multiple-output (MIMO) extended autofocus imaging method for distributed radar that efficiently enhances imaging performance. The proposed method unfolds an iterative imaging algorithm using deep denoisers with fewer unrolled iterations, enabling faster and more stable inference. Moreover, to fully exploit the information available across transmit and receive antennas, extending distributed radar to a MIMO configuration improves the imaging signal-to-noise ratio. At the same time, the direct-path signal between the transmit and receive antennas is explicitly incorporated into the data-fidelity term of the shift-kernel estimation problem, thereby mitigating autofocus degradation for long-range targets. Numerical simulations demonstrate consistently high peak signal-to-noise ratio and structural similarity index measure, as well as clear reconstruction of target shapes and salient features composed of multiple scattering points.