In complex maritime scenarios, multiple targets within the same radar beam often degrade the quality of inverse synthetic aperture radar (ISAR) imaging. Most existing methods typically leverage a single domain for target separation, hardly considering the relationships across multiple domains. To fully exploit the inter-domain interactions, we propose an end-to-end multi-domain interaction deep unrolling network based on a block-aware optimization model, termed MDIB-Net, to simultaneously achieve multi-target separation and echo reconstruction. Combining target structural features and deep unrolling techniques, this model-driven network effectively explores the correlations of the same target across different domains to achieve target separation. The MDIB-Net comprises cascaded iteration blocks, where each iteration block consists of an optimization block and a domain interaction (DIR) block. The optimization block solves the proposed block-aware multi-target separation function, leveraging low-rank properties, local similarity, and structural priors to capture the physical characteristics of targets. Moreover, a learnable module is incorporated in this block to discover the optimal transformation domain, thereby enhancing the structural prior of targets. The DIR block introduces a lightweight module to extract the semantic maps from the high-range resolution profiles (HRRPs) domain. The DIR block further incorporates a spatial-adaptive semantic guidance module, which takes the semantic maps as guidance, to refine the transformation domain features, effectively promoting cross-domain feature interactions. Additionally, the MDIB-Net achieves multi-target separation by dynamically adjusting its iterative strategy according to the predefined target number, demonstrating both robustness and flexibility. Simulated and measured experiments validate the effectiveness of the proposed method.

With the capacity to carry the orbital angular momentum (OAM), the vortex electromagnetic wave (VEMW) radar observes targets from a unique dimension. Since the VEMW radar introduces the azimuthal angle information, it certainly extends the potential of 3-D inverse synthetic aperture radar (3-D ISAR) imaging. Nevertheless, the large rotational angle and OAM mode number limit the application of reconstructing noncooperative targets’ spatial construction with high quality. To address it, this letter proposes a novel 3-D ISAR imaging method based on the migration through the azimuthal angle cells (MTAAC) compensation. First, we construct the MTAAC phase by forming and analyzing the rotational geometric model. Following that, the related MTAAC compensation phase is established. Then, we perform the CLEAN algorithm on the conventional ISAR image to estimate the range and cross-range of different scatterers. Finally, we iteratively extract the 3-D structure of the target by compensating the MTAAC within the VEMW ISAR image. The MTAAC domain is also provided to further confirm the condition and the coverage of the proposed method. The simulated experiments show that the proposed method achieves a 10.6% reconstruction error under 0-dB signal-to-noise ratio (SNR) scenarios, verifying the effectiveness of the proposed method.

Ground-penetrating radar (GPR) has become indispensable for subsurface reconstruction, especially in complex, multilayered infrastructure environments. Efficient and accurate estimation of permittivity and layer thickness, crucial for understanding these scenes, presents significant challenges. This article explores the integration and interaction between the back projection (BP) algorithm and full-wave inversion (FWI) to optimize reconstruction outcomes. We propose a ray-based refraction method and a high-order moment (HOM)-oriented local BP estimation, each aimed at improving efficiency from the critical perspectives of “delay” and “summation” in the BP algorithm, respectively, with a focus on the target layer. Crucially, the BP estimation results provide a reliable initial model for FWI, reducing the risk of local minima and decreasing the iteration count for more precise parameter estimation. FWI also compensates for the BP algorithm’s limitations in estimating parameters in nontarget layers. Furthermore, full-wave modeling (FWM) mitigates antenna effects prior to parameter estimation, thereby enhancing accuracy. During reconstruction, the BP algorithm primarily generates target imaging, while FWI provides detailed information about layer interfaces. This synergistic approach leverages the complementary strengths of both algorithms: the BP algorithm captures spatial information depicted by the targets observed in B-scans, while FWI incorporates detailed electromagnetic wave propagation along layer interfaces presented in A-scans via radar equations. Experiments systematically analyze the accuracy and effectiveness of the proposed approach using ideal simulation models, sandbox laboratory data, and road data from the Belgian Road Research Centre (BRRC) facilities. This comprehensive evaluation underscores the approach’s substantial potential in advanced geophysical surveys and environmental research.

Supervised learning for semantic segmentation has achieved impressive success in remote sensing, while this normally has a high demand on pixel-level ground truth from the testing images (target domain). Labeling data for semantic segmentation is labor-intensive and time-consuming. To reduce the workload of manual labeling, domain adaptation (DA) utilizes preexisting labeled images from other sources (source domain) to classify the images in the target domain. In this article, we propose a bispace alignment network for DA named BSANet. BSANet is designed to have a dual-branch structure which is able to extract features in the image domain and the wavelet domain simultaneously. To minimize the discrepancy between the source and target domains, we propose a bispace adversarial learning strategy. Specifically, BSANet employs two discriminators in different spaces, one aligning the source and target feature distributions, and the other helping the classification outputs render reasonable spatial layouts. The proposed method shows the ability to train an end-to-end network for semantic segmentation without using any label in the target domain. Extensive experiments and ablation studies are conducted in cross-city scenarios. Comparative experiments with several state-of-the-art DA methods show that our method achieves the best performance.

High-squint synthetic aperture radar (HS-SAR) imaging technology greatly enhances the flexibility of SAR. However, the existing HS-SAR imaging algorithms are generally based on the assumption that the illuminated target is static during the synthetic aperture formation. As a consequence, the state-of-the-art algorithms are unable to focus on moving targets well, thus leading to displaced and blurred images of the targets. A maritime ship is a typical kind of moving target with complex noncooperative motion, which has received much attention. Thus, it is a difficult but valuable issue to study HS-SAR imaging of ship targets. In this work, the influence of ship translation and fluctuation is theoretically analyzed. On the basis of these analyses, the new conception of “third range compression (TRC)” caused by ship translation is proposed, and the squint minimization (SM) operation is implemented for the compensation of TRC. Moreover, considering that the target Doppler parameters induced by ship fluctuation are related to its position, a novel method called WASH-CLEAN (watershed and CLEAN) is proposed to automatically focus ship scattering points with different swings. Finally, we propose an integrated modified range-Doppler (RD) imaging algorithm by combining the advantages of HS-SAR and inverse synthetic aperture radar (ISAR). The simulation results show the validity and effectiveness of the presented method.