Jiahui Chen

Accurately reconstructing synthetic aperture radar (SAR) images from incomplete echo measurements is the fundamental problem in simplifying SAR systems and reducing sensing costs. In solving this problem, the recently emerged deep learning SAR imaging concept shows high potential in offering both high accuracy and efficiency. However, such approaches face challenges in network topology designing, weights initialization, and dataset construction, which hinder their practical deployments. To address these problems, we propose a generative learning imaging framework, namely, generative learning imaging framework (GLIm), for millimeter wave (mmWave) SAR in tasks of sparsely sensing scenarios. In our scheme, the targeting images are generated from a specifically designed SAR image generator by mapping low-dimensional random noise to the scattering domain. In addition, the SAR image generator is trained based on a compound loss function where the incomplete echo measurements serve as the supervised signal. Unlike traditional algorithms, the algorithm proposed in this article solves the optimal solution purely through numerical propagation by online learning, avoiding the theoretical derivation of the iterative optimization process. Finally, experiments are carried out on both simulated and real-measured data, and both numerical and visual results demonstrate the viability of the proposed GLIm in the tasks of reconstructing mmWave SAR images from sparsely sampled echo measurements.

Accurate calibration of inter-channel delay and phase mismatches is essential for coherent imaging in multiple-input multiple-output (MIMO) through-the-wall radar (TWR) systems. Conventional calibration methods typically require anechoic chambers, whose high usage cost limits their practical applicability. In this paper, we present a robust multi-channel calibration method for through-wall MIMO radar systems, which compensates for delay and phase mismatches using measurement data acquired under non-ideal conditions, without requiring ideal environment setups. The effectiveness of the proposed method is validated through both simulations and experimental results, demonstrating significant improvements in image quality and target localization accuracy.

Bridges are crucial components of urban infrastructure, requiring continuous monitoring to ensure safety and longevity. With the advancement of Integrated Sensing and Communication (ISAC), base stations (BSs) can be leveraged for cost-effective and real-time bridge health monitoring. However, the sway of the antenna feeder system (AFS) of the BS under wind disturbances introduces phase errors in echoes, significantly degrading the accuracy of deformation estimation. To address this issue, this paper proposes an AFS sway phase suppression algorithm. First, an analytical echo model is developed to characterize the combined effects of bridge deformation and AFS sway. Then, based on a simplified echo model, the effects of AFS sway amplitude and angle on the target phase are derived. Building upon the echo model, the algorithm employs Variational Mode Decomposition (VMD) to extract the low-frequency sway phase and utilizes an optimization approach based on phase characteristics between the reference cells (RCs) and the target cell (TC) to estimate and suppress the AFS sway phase. Finally, numerical simulations are conducted to evaluate the influence of different parameters on the proposed algorithm. Real-world BS data serve to validate effectiveness of the proposed algorithm. Based on the experimental results, the proposed algorithm reduces the deformation estimation error by 95.6% (from 5.19 mm to 0.23 mm).

Compressive sensing (CS) technology has proven highly effective in rapid data acquisition and super-resolution target imaging for through-the-wall radar imaging (TWRI) applications. However, most existing CS-based TWRI techniques focus primarily on high-quality imaging of behind-wall targets, often neglecting the reconstruction of building layouts, which is essential for determining the relative positions of targets in unknown environments. To address this limitation, a structural-prior-guided CS framework is proposed for the joint reconstruction of building layouts and behind-wall targets. Specifically, first, distinct imaging models are developed for layouts and targets, accounting for their unique structural properties: layouts, referring to wall structures, typically manifest as extended, piecewise-continuous line-like structures, whereas targets manifest as compact, point-like structures. Building on these models, a unified constrained optimization problem is formulated by integrating: 1) the strong interchannel correlation of layout echoes, enforced via a low-rank regularization on the layout component and 2) structured sparsity priors tailored to both the layout and target images. Then, the resulting composite problem is efficiently solved using proximal gradient algorithm, yielding simultaneous reconstruction of the unknown building layouts and behind-wall targets. Finally, simulations and experimental results demonstrate the effectiveness of the proposed algorithm.

Multipath exploitation techniques have attracted considerable attention over the past decade, as they expand the detection coverage of conventional radar systems to Non-Line-of-Sight (NLOS) areas. However, existing methods rely on fully or partially known reflector surface information to achieve detection and localization of NLOS targets, which poses challenges for practical applications since scene information is often not available a priori. To overcome this issue, we present a multipath exploitation-based millimeter-wave (mmWave) multiple-input multiple-output (MIMO) imaging radar framework, which first performs three-dimensional (3D) environmental perception and then transfers the reflector surface parameters to assist NLOS target reconstruction. To implement this process, we explore several key problems and propose corresponding solutions. First of all, we propose a joint multiple linear array beamforming (JMLA-BF)-based imaging algorithm to achieve high-resolution sparse imaging as well as extract weak echoes of NLOS reflector surface. Then, an iteration algorithm is proposed for NLOS building layout recognition and relocation. Meanwhile, we propose an unambiguous direction-of-departure (DoD) and direction-of-arrival (DoA) estimation method for multipath signals from NLOS moving target using colocated MIMO radar. Finally, a path-oriented strategy is introduced to achieve NLOS target localization. Real experimental results demonstrate the effectiveness of the proposed framework and solutions in accurate 3D environmental perception and reliable NLOS target reconstruction.