A numerical procedure for analysis of electromagnetic scattering by a hypersonic cone-like body flying in the near space is presented. First, the fluid dynamics equation is numerically solved to obtain the electron density, colliding frequency, and the air temperature around the body. They are used to calculate the complex relative dielectric constants of the plasma sheath. Then the volume-surface integral equation method is adopted to analyze the scattering properties of the body plus the plasma sheath. The Backscattering Radar Cross-Sections (BRCS) for the body flying at different speeds, attack angles, and elevations are examined. Numerical results show that the BRCS at a frequency higher than 300 MHz is only slightly affected if the speed is smaller than 7 Mach. The BRCS at 1 GHz would be significantly reduced if the speed is greater than 7 Mach and is continuously increased, which can be attributed to the absorption by the lossy plasma sheath. Typically, the BRCS is influenced by 5~10 dBm for a change of attack angle within 0~15 degrees, or for a change of elevation within 30~70 km above the ground.

A dual-polarized compact Vivaldi antenna with high gain performance is proposed for tree radar applications. The proposed design introduces an array configuration consisting of two pairs of two Vivaldi elements to optimize the operating bandwidth and gain while providing dual-polarization capability. To enhance the gain of the proposed antenna over a certain frequency range of interest, directors and edge slots are incorporated into each Vivaldi element. To further enhance the overall antenna gain, a metal back reflector is used. The measurement results of the fabricated antenna show that the proposed antenna achieves a high gain of 5.5 to 14.8 dBi over broadband from 0.5 GHz to 3 GHz. Moreover, it achieves cross-polarization discrimination larger than 20 dB, ensuring high polarization purity. The fabricated antenna is used to detect and image the defects inside tree trunks. The results show that the proposed antenna yields a better-migrated image with a clear defect region compared to that obtained by a commercial Horn antenna.

Tree defect detection is crucial for the structural health screening of trees. Existing nondestructive testing (NDT) techniques for tree defect detection require time-consuming and labor-intensive measurement campaigns. This discourages their application for the routine structural health screening of whole populations of managed urban trees. To address this issue, this study proposes a deep-learning augmented stand-off radar scheme for contactless scanning of tree trunks and rapid detection of tree defects. In this scheme, the antenna is moved along a straight trajectory at a distance from the tree trunk to obtain the trunk’s B-scan. The obtained raw B-scan is then processed by a signal-processing framework specifically developed for revealing the scattering signatures of defects in B-scan, which achieves a 30 dB and 22 dB increase in the signal-to-clutter and noise ratio of the measurement data of tree trunk samples and living trees, respectively. Finally, the processed B-scan is input into a multilevel feature fusion neural network particularly designed for extracting the signature of the defect in the processed B-scan in real time. The developed scheme’s applications to the detection of defects in real fresh-cut tree trunks show that the stand-off radar scheme can detect tree defects with 96% accuracy. This stand-off radar scheme is the first contactless NDT technique for tree defect detection while operated on a straight trajectory and potentially can be integrated into the routine tree inspection workflow which is part of urban tree management.

Ground-penetrating radar (GPR) data are often contaminated by hardware and environmental clutter, which significantly affects the accuracy and reliability of target response identification. Existing supervised deep learning techniques for removing clutter in GPR data require generating a large set of clutter-free B-scans as labels for training, which are computationally expensive in simulation and unfeasible in real-world experiments. To tackle this issue, we propose a two-stage unsupervised learning-based clutter removal scheme, called ULCR-Net, to obtain clutter-free GPR B-scans. In the first stage of the proposed scheme, a diffusion model tailored for GPR data augmentation is employed to generate a diverse set of raw B-scans from the input random noise. With the augmented dataset, the second stage of the proposed scheme uses a contrastive learning-based generative adversarial network to learn and estimate clutter patterns in the raw B-scan. The clutter-free B-scan is then obtained by subtracting the clutter pattern from the raw B-scan. The training of the two-stage network only requires a small set of raw B-scans and clutter-only B-scans that are readily available in real-world applications. Extensive experiments have been conducted to validate the effectiveness of the proposed method. Results on simulation and measurement data demonstrate that the proposed method has superior clutter removal accuracy and generalizability and outperforms existing algebraic techniques and supervised learning-based methods with limited training data by a large margin. With its high clutter suppression capability and low training data requirements, the proposed method is well-suited to remove clutter and restore target responses in real-world GPR applications.

Ground-penetrating radar (GPR) has emerged as a prominent tool for imaging internal defects in cylindrical structures, such as columns, utility poles, and tree trunks. However, accurately reconstructing both the shape and permittivity of the defects inside cylindrical structures remains challenging due to complex wave scattering phenomena and the limited accuracy of the existing signal processing and deep learning techniques. To address these issues, this study proposes a migration-assisted deep learning scheme for reconstructing the shape and permittivity of defects within cylindrical structures. The proposed scheme involves three stages of GPR data processing. First, a dual-permittivity estimation network (DPE-Net) extracts the permittivity values of the defect and the cylindrical structure, the latter of which is estimated with the help of a novel structural similarity index measure (SSIM)-based autofocusing technique (AFT). Second, a modified Kirchhoff migration incorporating the extracted permittivity of the cylindrical structure maps the signals reflected from the defect to the imaging domain. Third, a shape reconstruction network (SR-Net) processes the migrated image to recover the precise shape of the defect. The image of the interior defect is finally obtained by combining the reconstructed shape and extracted permittivity of the defect. The proposed scheme is validated using both synthetic and experimental data from a laboratory trunk model and real tree trunk samples. Comparative results show superior performance over existing deep learning methods, while generalization tests on live trees confirm its feasibility for in-field deployment. The underlying principle can further be applied to other circumferential GPR imaging scenarios. The code and database are available at https://github.com/jwqian54/Migration-Assisted-DL

The integrity of ground-penetrating radar (GPR) B scans is essential for reliable interpretation of the object's information. However, acquiring complete and high-quality B scans is often hindered by field obstructions and measurement inefficiencies, leading to severely corrupted data. In this study, we propose a multi-sparsity transformer model, which jointly models global contextual dependencies and local structural information for recovering missing traces in corrupted B-scan. A self-supervised learning strategy is further introduced to enable effective training without requiring complete B-scan data as ground truth. The proposed approach is evaluated on both synthetic and measurement datasets, including linear scanning for underground sensing and circumferential scanning for defect detection in cylindrical objects. Experimental results demonstrate superior reconstruction performance over existing deep learning methods, maintaining high accuracy even with up to 90% randomly missing traces and large consecutive missing regions. These results, together with downstream task validation, indicate that the proposed approach can substantially improve the quality of corrupted GPR data and enhance its practical utility in challenging real-world acquisition environments. The code and database will be shared on GitHub upon acceptance of the manuscript.