Efficient Physics-Based Learned Reconstruction Methods for Real-Time 3D
  Near-Field MIMO Radar Imaging
Irfan Manisali Okyanus Oral Figen S. Oktem
Near-field multiple-input multiple-output (MIMO) radar imaging systems have
recently gained significant attention. In this paper, we develop novel
non-iterative deep learning-based reconstruction methods for real-time
near-field MIMO imaging. The goal is to achieve high image quality with low
computational cost at compressive settings. The developed approaches have two
stages. In the first approach, physics-based initial stage performs adjoint
operation to back-project the measurements to the image-space, and deep neural
network (DNN)-based second stage converts the 3D backprojected measurements to
a magnitude-only reflectivity image. Since scene reflectivities often have
random phase, DNN processes directly the magnitude of the adjoint result. As
DNN, 3D U-Net is used to jointly exploit range and cross-range correlations. To
comparatively evaluate the significance of exploiting physics in a
learning-based approach, two additional approaches that replace the
physics-based first stage with fully connected layers are also developed as
purely learning-based methods. The performance is also analyzed by changing the
DNN architecture for the second stage to include complex-valued processing
(instead of magnitude-only processing), 2D convolution kernels (instead of 3D),
and ResNet architecture (instead of U-Net). Moreover, we develop a synthesizer
to generate large-scale dataset for training with 3D extended targets. We
illustrate the performance through experimental data and extensive simulations.
The results show the effectiveness of the developed physics-based learned
reconstruction approach in terms of both run-time and image quality at highly
compressive settings. Our source codes and dataset are made available at
GitHub.