Non-Line-of-Sight mmW SAR Imaging With Equivariant Adaptive Threshold Learning

Abstract

High-precision 2-D/3-D Synthetic Aperture Radar (SAR) image reconstruction from the indirect scattered echoes of hidden targets represents a core technical challenge in millimeter-wave (mmW) Non-Line-of-Sight (NLOS) environmental perception. Deep learning approaches have demonstrated exceptional performance in SAR imaging. However, existing methods are predominantly designed for Line-of-Sight (LOS) scenarios, where clean LOS simulation signals can be acquired for training purposes, a condition often difficult or impossible to meet in NLOS imaging due to complex multipath environments and noise. To tackle this issue within specific NLOS configurations, particularly those involving strong specular reflections from discrete, isolated hidden objects, we propose an Equivariant Imaging (EI) framework tailored for mmW SAR. The EI framework is a fully self-supervised learning approach that leverages the group invariance present in signal distributions, enabling robust image reconstruction from partial NLOS measurements contaminated with noise and multipath artifacts. In our method, the reconstruction function is based on a deep unfolding network with Total Variation (TV) constraints, mapping the NLOS scattered echoes to the target image. Moreover, we introduce an Adaptive Peak Convolution Network (APConv) into the reconstruction process to dynamically adjust thresholds, replacing traditional fixed-threshold methods. This enhances imaging flexibility and quality under these defined NLOS conditions. Finally, we validate the proposed method using various NLOS echo data collected through an experimental mmW system. Numerical and visual results both demonstrate the effectiveness of our approach for NLOS mmW SAR imaging tasks. The proposed EI framework thus offers a promising approach for advancing NLOS mmW SAR perception capabilities, particularly for environments and target configurations aligning with those investigated and supported by our current experiments.