Search Results (185)

In this paper, we present a deep neural network–based approach for computing radar cross section (RCS) over a wide frequency band and a broad range of incident angles. The proposed network, termed WBRCS-Net, is designed to converge to the solution of the method of moments (MoM) formulation by minimizing a mean-squared residual loss without explicitly solving the MoM linear system, thereby avoiding the numerical instabilities commonly encountered in conventional iterative solvers. Moreover, by using only the frequency and incident angle as inputs, WBRCS-Net enables wideband RCS prediction over a broad range of incident angles while substantially simplifying the network architecture. The performance of WBRCS-Net is evaluated on perfectly electrically conducting (PEC) spheres and cubes and is compared with the Maehly approximation based on Chebyshev polynomials, using monostatic RCS over a frequency range of 2–12 GHz and an incident-angle range of 0°∼90°. Experimental results demonstrate that, once trained, WBRCS-Net enables stable wideband RCS computation over a wide range of incident angles with instantaneous inference speed, achieving a minimum mean-squared error (MSE) on the order of ${10}^{-14}$ relative to reference MoM solutions. Full article

Micro-motion features of targets, such as nutation and coning, play a crucial role in radar-based target recognition and classification. This paper addresses the challenge of extracting three-dimensional micro-motion parameters from smooth symmetric nutating cone targets using monostatic radar. Unlike conventional methods that rely on tail-fin structures, the proposed approach leverages the micro-Doppler characteristics of both fixed and sliding scattering points on the cone. The motion model of a nutating cone is established, and the expressions for micro-Doppler frequency shifts are derived. Based on the visibility of scattering points at the cone bottom, two categories of echoes are defined: those containing one or two scattering points. For each category, tailored signal processing methods are developed to estimate micro-motion parameters, including nutation angle, precession angle, coning frequency, wobble frequency, and geometric dimensions. Simulations under both noise-free and noisy conditions validate the effectiveness of the proposed method, demonstrating its robustness and accuracy in 3-D micro-motion feature extraction. Full article

Compared with conventional monostatic radar systems, space-air bistatic frequency diverse array (FDA) radar exhibits superior anti-jamming capability and enhanced early-warning performance for weak and maneuvering targets. However, the complex bistatic configuration and the high velocity of spaceborne platforms introduce several challenges, including range migration (RM), Doppler spread (DS), and Doppler frequency migration (DFM). In particular, frequency offsets among FDA array elements exacerbate inter-channel Doppler mismatches, significantly reducing the coherent integration gain and consequently degrading detection performance. To address these issues, this article establishes a target echo model within a three-dimensional coordinate framework and provides an analysis of the different order terms. Subsequently, the SOKT is implemented to eliminate first- and second-order range migrations as well as the coupling induced by velocity ambiguity. Thereafter, the MDF is employed in the slow-time domain to compress Doppler spread and restore coherent gain. Simulation results verify that the SOKT-MDF approach effectively mitigates the effects of target velocity and acceleration, alleviates the Doppler spread (DS) problem, and significantly improves detection performance while maintaining low computational complexity. Full article

Microwave imaging (MWI) is a non-invasive technique for visualizing the anomalies of biological tissues. The imaging process is accomplished by comparing the electrical parameters of healthy tissues and malignant tissues. This work introduces a microwave imaging system for tumor detection in breast tissue. The experiment is performed in a homogeneous background medium, where a high dielectric contrast material is used to mimic the tumor. The proposed imaging system is experimentally evaluated for multiple tumor locations and sizes using a horn antenna. Reflection coefficients obtained from the monostatic configuration of the horn antenna are used for image reconstruction. The evaluation metrics, such as localization error, absolute area error, DICE score, Intersection over Union (IoU), precision, accuracy, sensitivity and specificity, are computed from the reconstructed image. A modified version of the beamforming algorithm improves the quality of reconstructed images by providing a minimum accuracy of 96% for all test cases, with an evaluation time of less than 48 s. The proposed methodology shows promising results under a controlled environment and can be implemented for clinical applications after adequate biological studies. This methodology can be used to calibrate any antenna system or phantom, as it has high contrast in conductivity, leading to better imaging. The present study contributes to Sustainable Development Goal (SDG) 3 by ensuring healthy lives and promoting wellbeing for all ages. Full article

Land cover mapping is a crucial component of the Copernicus Land Monitoring Service, but existing products underestimate urbanized areas and small-scale man-made objects, limiting their ability to capture the complexity of built environments. Long-baseline monostatic–bistatic Synthetic Aperture Radar (SAR) images, such as the ones that will be made available by the upcoming PLATiNO-1 mission, have the potential to contribute to the detection of the mentioned targets, e.g., by traditional supervised classification approaches. Since bistatic measurements from the PLATiNO-1 mission are not yet available, repeat-pass COSMO-SkyMed second generation (CSG) images collected with different incidence angles are employed to emulate the expected diversity of future monostatic–bistatic products. A complete classification pipeline is developed, and a structured dataset of 48 features is built, combining intensity, polarimetric, spatial, and textural descriptors to train an XGBoost model to identify urban targets within a representative area in Italy. The results demonstrate stable performance, with F1 scores around 0.73 and true positive rates close to 80%, showing good agreement with reference data and confirming the feasibility of the proposed methodology. Although conceived as a proof of concept, the study shows that integrating multi-angle information into classification tasks can improve the detection of man-made structures and provide an additional information layer to be integrated with Copernicus services. Full article

An efficient deep-learning-based framework for optimization-based inverse design of electromagnetic metasurface design is proposed in this paper. A novel unit-cell parameterization strategy generates 16-element structures via symmetry operations governed by ten geometric parameters, overcoming the inefficiencies of pixel-based representations. A dataset of 16,000 parameter–reflection phase pairs is constructed, and a hybrid model combining Convolutional Neural Network (CNN), attention mechanisms, and the Kolmogorov–Arnold Network (KAN) is developed for broadband response prediction. The coefficient of determination (R²) of the proposed model is 0.8837 in the 2–18 GHz band, which is 44.87% higher than the R² without KAN. The proposed chessboard metasurface achieves a 10 dB monostatic radar cross-section (RCS) reduction under normal incidence over a wide frequency band from 7.4 to 15.2 GHz, corresponding to a relative bandwidth of 69%. This approach provides a generalizable, data-efficient solution for intelligent metasurface design. Full article

A mechanically reconfigurable phased array antenna (MRPA) with switchable radiation and scattering characteristics is presented. By adjusting the height of each array element, a continuous aperture phase response is achieved, enabling mechanical beam steering without electronic phase shifters. In the radiation mode, a height-induced phase gradient is used to steer the beam, while in the scattering mode, the same height–phase mapping mechanism produces multi-element phase cancellation for radar cross-section (RCS) reduction. An 8 × 8 prototype operating at 7.9 GHz is designed and validated. The array achieves beam steering up to ±45° with a peak realized gain of 21.5 dBi and an aperture efficiency of 87.6%. Moreover, more than 10 dB monostatic RCS reduction is obtained over a wide frequency range from 3 to 38 GHz. The proposed design provides a unified mechanical approach for radiation enhancement and scattering suppression in multifunctional phased arrays. Full article

The analysis of ground-penetrating radar data generally relies on the visual identification of structures on selected profiles and their interpretation in terms of buried features. In simple cases, inverse modelling of the acquired data set can facilitate interpretation and reduce subjectivity. These methods suffer from severe restrictions due to antenna resolution limits, which prevent the identification of tiny structures, particularly in forensic, stratigraphic, and engineering applications. Here, we describe a technique to obtain a high-resolution characterization of the underground, based on the forward modelling of individual traces (A-scans) of selected radar profiles. The model traces are built by superposition of Ricker wavelets with different polarities, amplitudes, and arrival times and are used to create reflectivity diagrams that plot reflection amplitudes and polarities versus depth. A thin bed is defined as a layer of higher or lower permittivity relative to the surrounding material, such that the top and bottom reflections are subject to constructive interference, determining the formation of an anomalous peak in the trace (tuning effect). The proposed method allows the detection of ultra-thin layers, well beyond the Rayleigh vertical resolution of GPR antennas. This approach requires a preliminary estimation of the instrumental uncertainty of common monostatic antennas and takes into account the frequency-dependent attenuation, which causes a spectral shift of the dominant frequency acquired by the receiver antenna. Such a quantitative approach to analyzing radar data can be used in several applications, notably in stratigraphic, forensic, paleontological, civil engineering, heritage protection, and soil stratigraphy applications. Full article

The prediction accuracy of the General Circulation Model (GCM) is influenced by the effectiveness of gravity wave activity parameterization. Although research focuses on small-scale horizontal gravity wave activity as a carrier for energy and momentum coupling between atmospheric layers, routine observations of horizontal gravity wave activity on scales less than a dozen kilometers are scarce due to limitations in observational instruments. This paper presents a method for observing small-scale horizontal gravity waves using monostatic Rayleigh lidar, along with the associated data processing workflow. The data processing results indicate that the observed gravity waves generally exhibit wavelengths less than 3 km and phase velocities less than 0.5 m/s. Furthermore, the annual variation in small-scale horizontal gravity waves displays a semi-annual oscillation (SAO), like that observed in medium- and large-scale waves. This suggests that the observed gravity waves originate from secondary gravity waves resulting from saturation dissipation or breaking. Full article

Right-angle dihedral structures produce strong, highly correlated returns that dominate radar cross-section (RCS) and image signatures. Conventional absorbers or random coding metasurfaces often lose effectiveness across wide frequency bands and angles, and cannot adequately suppress the corner-induced hot spots. We propose a wideband super-dispersion encoded surface (SDES) conformally applied to dihedral facets. The approach co-designs input-admittance for absorption with a deterministic super-dispersion phase sequence to redistribute energy spectrally and angularly, thereby decorrelating the returns. We implement SDES on a thin composite panel and evaluate it on canonical dihedral and dihedral–cylindrical hybrid configurations. Unlike diffuse or random coding schemes, SDES enforces broadband, angle-stable dispersion with a deterministic sequence that specifically addresses corner singularity scattering. We also introduce perceptual-hashing as an imaging-domain metric to link RCS control with observable radar-image changes. From 12–18 GHz, SDES reduces the average monostatic RCS by 9.6 dB on a right-angle dihedral. In dihedral–cylindrical hybrids, SDES removes the corner hot spots and drives the radar-image similarity index down to 0.31, confirming substantial alteration of scattering signatures. Full article

Terrestrial Laser Scanners (TLS), as monostatic LiDAR systems, emit and receive laser pulses through a single aperture, which ensures the simultaneous measurement of signal geometry and intensity. The relative intensity of a signal, defined as the ratio of received to transmitted power, directly describes the strength and quality of the reflected signal and the corresponding radiometric uncertainty of individual points. The LiDAR range equation provides the physical connection for characterizing signal strength as a function of reflectivity and other spatial parameters. In this research, theoretical developments of the texture-dependent LiDAR range equation, in conjunction with a neural network method, are presented. The two-step approach aims to improve the accuracy of signal intensities by enhancing signal reflectivity estimation and the precision of signal intensities by reducing their sensitivity to variations in spatial characteristics—range and incidence angle. This establishes the intensity as the standard fourth dimension of the 3D point cloud based on the inherent target quality. For validation, four terrestrial laser scanners—Leica ScanStation P50, Leica ScanStation C10, Leica RTC360, and Trimble X9—are evaluated. Results demonstrate significant improvements of at least 40% in accuracy and 97% in precision for the color intensities of individual points across the devices. This research enables a 4D TLS point cloud calibration framework for further investigations on other internal and external geometries of targets (target materials, roughness, albedo, and edgy and tilted surfaces), which allows the standardization of radiometric values. Full article

In this paper, a 3D-printed S-Band corrugated horn antenna with X-Band radar cross section (RCS) reduction is investigated. This work demonstrates effective RCS reduction at the X-band through the application of the phase cancellation principle. Specifically, the corrugated horn antenna is partitioned into eight identical sections, with three discrete height offsets introduced between them. The reflection phase cancellation, which can be attained through the path difference introduced by a designed height step among different regions, leads directly to a consequent suppression of scattered waves. The proposed low-RCS corrugated horn antenna is monolithically fabricated using stereolithography appearance (SLA) 3D printing technology, followed by a surface metallization process. The measured results demonstrate that the proposed antenna operates over the frequency band of 2.34–3.3 GHz in the S-band with good impedance matching, exhibiting a peak gain of 11.7 dB. Furthermore, the monostatic RCS of the antenna under normal incidence for both x- and y-polarizations exhibits a significant reduction of over 10 dB within the frequency range of 8.7–12.0 GHz and 8.2–12.0 GHz, respectively. This indicates that effective stealth performance is achieved across the majority of the X-band. The proposed design integrates exceptional out-of-band RCS reduction, low cost, light weight, and high efficiency, making it a promising candidate for radar stealth system applications. Full article

Benefiting from the advantages of unmanned aerial vehicle (UAV) platforms such as low cost, rapid deployment capability, and miniaturization, the application of UAV-borne synthetic aperture radar (SAR) has developed rapidly. Utilizing a self-developed monostatic Miniaturized SAR (MiniSAR) system and a bistatic MiniSAR system, our team conducted multiple imaging missions over the same vehicle equipment display area at different times. However, system disparities and time-varying factors lead to a mismatch between the distributions of the training and test data. Additionally, small ground vehicle targets under complex background clutter exhibit limited size and weak scattering characteristics. These two issues pose significant challenges to the precise detection of small ground vehicle targets. To address these issues, this article proposes a domain-unified adaptive target detection framework (DUA-TDF). The approach consists of two stages: image-to-image translation and feature extraction and target detection. In the first stage, a multi-scale detail-aware CycleGAN (MSDA-CycleGAN) is proposed to align the source and target domains at the image level by achieving unpaired image style transfer while emphasizing both global structure and local details of the generated images. In the second stage, a cross-window axial self-attention target detection network (CWASA-Net) is proposed. This network employs a hybrid backbone centered on the cross-window axial self-attention mechanism to enhance feature representation, coupled with a convolution-based stacked cross-scale feature fusion network to strengthen multi-scale feature interaction. To validate the effectiveness and generalization capability of the proposed algorithm, comprehensive experiments are conducted on both self-developed monostatic/bistatic SAR datasets and public dataset. Experimental results demonstrate that our method achieves an mAP50 exceeding 90% in within-domain tests and maintains over 80% in cross-domain scenarios, demonstrating exceptional and robust detection performance as well as cross-domain adaptability. Full article

Reducing the radar cross section (RCS) of airborne platforms is essential for stealth and survivability in modern defense systems. Frequency-selective surfaces (FSSs) offer band-specific transmission and reflection characteristics. Thus, they are highly suitable for radome integration. However, most of the conformal FSS radome designs proposed in the literature rely on multilayered or geometrically complex configurations. Although these designs are effective, their fabrication cost is high, which limits their practical applicability. In this study, a conformal-bandpass FSS operating in the 8–12 GHz frequency range is proposed, emphasizing both electromagnetic efficiency and manufacturability. The unit cell was designed with a simple yet effective ring–patch geometry and optimized through full-wave simulations to ensure stable bandpass behavior. The structure was then integrated into a conical radome, and its performance was evaluated by conducting monostatic RCS simulations. In this way, the scattering characteristics of the FSS-based radome were analyzed. The results demonstrate that the FSS-based radome could consistently achieve lower RCSs across the desired spectrum. Therefore, the proposed design not only reduces radar visibility but also provides a scalable and cost-effective solution. We believe that it may offer a practical pathway for next-generation low-observable radome technologies. Full article

Phased array technology can be realized with directional control with fixed beam steering. However, its directionally dependent beam pattern limits the efficiency of suppressing undesirable distance interference. This paper presents a guided wave frequency diverse array-based damage location method for thin-walled structures. Firstly, a guided wave frequency diverse array signal model is derived with a relatively small frequency increment that can achieve distance–direction two-dimensional focusing. Secondly, three types of receiving arrays, including a monostatic array, following array, and symmetric array, are constructed to achieve the maximum damage-induced signal amplitude. Finally, a two-dimensional multiple signal classification (MUSIC)-based damage location method is applied for damage imaging in thin-walled structures. Simulations on an aluminum plate and the experiments on an epoxy laminate plate demonstrate the validity and effectiveness of the proposed method. Full article