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 paper 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-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 full-wave inversion, 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 non-target layers. Furthermore, full-wave modeling 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 facilities. This comprehensive evaluation underscores the approach’s substantial potential in advanced geophysical surveys and environmental research.

With the capability of capturing a target’s two-dimensional information, Inverse Synthetic Aperture Radar (ISAR) imaging is widely used in Radar Automatic Target Recognition. However, changes in the ship target’s attitude can lead to the scatterers’ rotation, occlusion, and angle glint, reducing the accuracy of ISAR image recognition. To solve this problem, we proposed a Triangle Preserving level-set-assisted Triangle-Points Affine Transform Reconstruction (TP-TATR) for ISAR ship target recognition. Firstly, three geometric points as initial information were extracted from the preprocessed ISAR images based on the ship features. Combined with these points, the Triangle Preserving level-set (TP) method robustly extracted the fitting triangle of targets depending on the intrinsic structure of the ship target. Based on the extracted triangle, the TP-TATR adjusted all the ship targets from the training and test data to the same attitude, thereby alleviating the attitude sensitivity. Finally, we created templates by averaging the adjusted training data and matched the test data with the templates for recognition. Experiments based on the simulated and measured data indicate that the accuracies of the TP-TATR method are 87.70% and 90.03%, respectively, which are higher than those of the contrast algorithms and have a statistical difference. These demonstrate the effectiveness and robustness of our proposed TP-TATR method.

In real-world situations, multiple targets may locate in the same radar antenna beam, resulting in degraded readability of inverse synthetic aperture radar (ISAR) imaging. Due to the different motions of multiple targets, the existing mono-target imaging methods generally lead to tight overlap and severe defocusing. To tackle this problem, this paper proposes a two-step method based on dual-precision optimization for multi-target ISAR imaging. First, the Radon transform estimates the range walks of different targets, subsequently generating the proposed coarse motion compensation transforms (MCTs). Moreover, the short imaging interval commonly brings strong similarity within adjacent range profiles. This internal property motivates us to model the multi-target coarse separation as a problem jointly constrained by local similarity and low-rank property. Such a double-constrained problem is then solved by incorporating the alternating direction method of multipliers with linearized alternative direction method with adaptive penalty (ADMM-LADMAP) framework. Through these procedures, range profiles of different targets are partially separated, allowing the correlation-based range alignment methods to estimate the range walk with higher accuracy. With the help of these accurate estimations, MCTs are modified, and the sparsity of the ISAR image is simultaneously enhanced. The sparse prior is thus integrated into the previous optimization to convert coarse separation to a more precise one so as to achieve entire separation. Finally, phase autofocus and cross-range compression are carried out on the completely isolated range profiles to yield well-focused ISAR images. Experiments based on both simulated and measured data demonstrate the effectiveness of the proposed method.

High-resolution range profile (HRRP) recognition serves as a foundational task in radar automatic target recognition (RATR), enabling robust classification under all-day and all-weather conditions. However, existing approaches often struggle to simultaneously capture the multi-scale spatial dependencies and global spectral relationships inherent in HRRP signals, limiting their effectiveness in complex scenarios. To address these limitations, we propose a novel multi-scale domain perception network tailored for HRRP-based target recognition, called MSDP-Net. MSDP-Net introduces a hybrid spatial–spectral representation learning strategy through a multiple-domain perception HRRP (DP-HRRP) encoder, which integrates multi-head convolutions to extract spatial features across diverse receptive fields, and frequency-aware filtering to enhance critical spectral components. To further enhance feature fusion, we design a hierarchical scale fusion (HSF) branch that employs stacked semantically enhanced scale fusion (SESF) blocks to progressively aggregate information from fine to coarse scales in a bottom-up manner. This architecture enables MSDP-Net to effectively model complex scattering patterns and aspect-dependent variations. Extensive experiments on both simulated and measured datasets demonstrate the superiority of MSDP-Net, achieving 80.75% accuracy on the simulated dataset and 94.42% on the measured dataset, highlighting its robustness and practical applicability.

Inverse Synthetic Aperture Radar (ISAR) images are extensively used in Radar Automatic Target Recognition (RATR) for non-cooperative targets. However, acquiring training samples for all target categories is challenging. Recognizing target classes without training samples is called Zero-Shot Learning (ZSL). When ZSL involves multiple modalities, it becomes Multi-modal Zero-Shot Learning (MZSL). To achieve MZSL, a framework is proposed for generating ISAR images with optical image aiding. The process begins by extracting edges from optical images to capture the structure of ship targets. These extracted edges are used to estimate the potential locations of the target’s scattering centers. Using the Geometric Theory of Diffraction (GTD)-based scattering center model, the edges’ ISAR images are generated from the scattering centers. Next, a mapping is established between the edges’ ISAR images and the actual ISAR images. Neighbor-Adapted Local Linear Embedding (NALLE) generates pseudo-ISAR images for the unseen classes by combining the edges’ ISAR images with the actual ISAR images from the seen classes. Finally, these pseudo-ISAR images serve as training samples, enabling the recognition of test samples. In contrast to the network-based approaches, this method requires only a limited number of training samples. Experiments based on simulated and measured data validate the effectiveness.

High-resolution range profile (HRRP)-based radar automatic target recognition (RATR) is crucial in capturing target structural characteristics across all-day and all-weather environments. However, existing HRRP-based RATR methods struggle to adapt to diverse target aspects and backscattering characteristics due to insufficient modeling of spatial structures and spectral periodicity. To address these limitations, we propose the MSHP-Net, a multi-scale hybrid perception network with granularity decoupling and spectral enhancement for HRRP target recognition. The MSHP-Net employs a multi-scale hybrid perception (HP) encoder to jointly capture spatial-spectral domain features by combining spatial feature decoupling and spectral feature recalibration. Specifically, we extract multi-granularity spatial features and decouple them into granularity-invariant and granularity-variant components based on an adaptive singular value decomposition (SVD). This process explicitly enhances structural consistency and preserves fine-grained variations, effectively modeling varying target aspects and mitigating structural distortions inherent in HRRP data. To capture global spectral correlations and periodic scattering characteristics, we recalibrate the spectral distribution and apply spectrally enhanced attention, emphasizing the critical spectral bands and suppressing background noise. To promote multi-scale hybrid features interaction, we introduce a hierarchical affinity-guided gating to propagate cross-scale relevant information flow, balancing low-level details with high-level semantics for more comprehensive feature representations. Finally, we aggregate scale-wise features and predict the final classification. Comparative experiments on both simulated and measured datasets validate the effectiveness of the proposed network.