Search Results (176)

Microwave, millimeter-wave, and terahertz devices are fundamental to modern 5G/6G communications, automotive imaging radar, and sensing systems. As essential RF front-end elements, passive microwave array components on static platforms remain constrained by fixed geometry and single-frequency excitation, leading to limited spatial resolution and weak interference suppression. Phase-steered arrays offer angular control but lack range-dependent response, preventing true two-dimensional focusing. Frequency-Diverse Array Multiple-Input Multiple-Output (FDA-MIMO) architectures introduce element-wise frequency offsets to enrich spatial–spectral degrees of freedom, yet conventional linear or predetermined nonlinear offsets cause range–angle coupling, periodic lobes, and restricted beamforming flexibility. Existing optimization strategies also tend to target single objectives and insufficiently address target- or scene-induced perturbations. This work proposes a nonlinear frequency-offset design for passive microwave arrays using a Dingo–Gray Wolf hybrid intelligent optimizer. A multi-metric fitness function simultaneously enforces sidelobe suppression, null shaping, and frequency-offset smoothness. Simulations in static scenarios show that the method achieves high-resolution two-dimensional focusing, enhanced interference suppression, and stable performance under realistic spatial–spectral mismatches. The results demonstrate an effective approach for improving the controllability and robustness of passive microwave array components on static platforms. Full article

Background: Single plane-wave ultrasound imaging (SPWI) enables acquisition speeds exceeding 1000 Hz, making it suitable for real-time applications requiring high temporal resolution. However, SPWI suffers from clutter artifacts, such as multipath reverberations, which degrade image contrast and diagnostic reliability. Methods: To address this limitation, we propose an unsupervised beamforming approach based on optimized deep coherence loss (UBF-DCL_opt), which adaptively performs signal coherence computation according to the inter-frame decorrelation of plane-wave data. In addition, optimal plane-wave frames for coherence loss calculation are adaptively determined by physics-based criteria that account for steering angle and broadband pulse characteristics. To evaluate the proposed method, simulation, phantom and in vivo studies were conducted. For training and validation, publicly available datasets and data acquired from a fabricated clutter phantom were employed. Results: Experimental results demonstrated that the proposed UBF-DCL_opt achieved contrast-to-noise ratio (CNR) improvements of 22% in phantom experiments and 32% in the in vivo studies compared to an unsupervised beamforming method using fixed deep coherence loss (UBF-DCL). Conclusions: These results demonstrate that the physics-informed unsupervised approach significantly suppresses reverberation artifacts while maintaining high spatiotemporal resolution, thereby enabling enhanced diagnostic accuracy in real-time ultrasound imaging. Full article

Background/Objectives: Single plane wave imaging (SPWI) offers ultrafast acquisition rates suitable for real-time ultrasound imaging applications; however, its image quality is compromised by beamforming artifacts such as sidelobe and grating lobe interferences. Methods: In this paper, we introduce an unsupervised beamforming framework based on adaptive deep coherence loss (DCL-A), which employs linear (${\alpha }_{\mathrm{linear}}$) or nonlinear weighting (${\alpha }_{\mathrm{nonlinear}}$) within the coherence loss function to enhance the artifact suppression and improve overall image quality. During training, the adaptive weight (α) is determined by the angular distance between the input and target PW frames, assigning lower α values for smaller distances and higher α values for larger distances. Therefore, this adaptability enables the method to surpass conventional DCL (no weighting) by emphasizing the different spatial correlation characteristics of mainlobe and sidelobe signals. To assess the performance of the proposed method, we trained and validated the network using publicly available datasets, including simulation, phantom and in vivo images. Results: In the simulation and phantom studies, the DCL-A with ${\alpha }_{\mathrm{nonlinear}}$ outperformed the comparison methods (i.e., conventional DCL and DCL-A with ${\alpha }_{\mathrm{linear}}$) in terms of peak range sidelobe level (PRSLL), achieving 7 dB and 14 dB greater sidelobe suppression, respectively, while maintaining a comparable full width at half maximum (FWHM). In the in vivo study, it achieved the highest contrast resolution among the comparison methods, yielding 2% and 3% improvements in generalized contrast-to-noise ratio (gCNR), respectively. Conclusions: These results demonstrate that the proposed deep learning-based beamforming framework can significantly enhance SPWI image quality without compromising frame rate, indicating promising potential for high-speed, high-resolution clinical applications such as cardiac assessment and real-time interventional guidance. Full article

Elevation Digital Beamforming (DBF) technology is key to achieving high-resolution wide-swath (HRWS) imaging in spaceborne Synthetic Aperture Radar (SAR) systems. However, multi-channel DBF-SAR systems face a prominent conflict between the need for real-time channel error calibration and the constraints of limited on-board hardware resources. To address this bottleneck, this paper proposes a real-time channel error calibration method based on Fast Fourier Transform (FFT) pulse compression and introduces a “calibration-operation” dual-mode control with a parameter-persistence architecture. This scheme decouples high-complexity computations by confining them to the system initialization phase, enabling on-board, real-time, closed-loop compensation for multi-channel signals with low resource overhead. Test results from a high-performance Field-Programmable Gate Array (FPGA) platform demonstrate that the system achieves high-precision compensation for inter-channel amplitude, phase, and time-delay errors. In the 4-channel system validation, the DBF synthesized signal-to-noise ratio (SNR) improved by 5.93 dB, reaching a final SNR of 44.26 dB. This performance approaches the theoretical ideal gain and significantly enhances the coherent integration gain of multi-channel signals. This research fully validates the feasibility of on-board, real-time calibration with low resource consumption, providing key technical support for the engineering robustness and efficient data processing of new-generation SAR systems. Full article

Underwater acoustic imaging is essential in marine science and engineering, enabling high-resolution detection and characterization of underwater structures and targets. However, conventional deconvolution beamforming methods using broadband signals often suffer from model mismatch, inter-frequency interference, and limited noise robustness. To overcome these challenges, this study rigorously analyzes the point spread function of the imaging system and introduces Acoustic Frequency Comb (AFC) excitations to enhance resolution. By exploiting the autocorrelation characteristics of AFC signals and optimizing key parameters, imaging artifacts are effectively suppressed and the main-lobe width is narrowed, resulting in a 50% improvement in range resolution. Comparative analyses identify the Richardson–Lucy algorithm as the most effective in enhancing azimuthal resolution and maintaining robustness under array perturbations and low signal-to-noise ratios. Parametric studies further demonstrate that AFC excitation outperforms conventional linear frequency modulated pulses, achieving a 30% main-lobe width reduction, 10 dB sidelobe suppression, and a 14 dB noise decrease. Finally, tank experiments confirm the simulation results, showing that accurate PSF modeling enabled by AFC ensures high angular resolution. The discrete spectral structure facilitates more effective separation of signal and noise during iterative deconvolution, while excellent autocorrelation characteristics guarantee high range resolution, yielding superior overall imaging performance. Full article

A circular array transducer with high frequency and small aperture size is highly desired for intravascular ultrasound (IVUS) imaging application. With the breakthrough of array transducer techniques, high-frequency circular array transducers with the advantages of high frame rate and high resolution have been developed and manufactured. Focusing on the development of a matched high-frequency imaging algorithms, this study introduces apodization functions into 55 MHz circular-array IVUS imaging, proposes a circular-array-specific apodization model, and breaks the lateral-resolution limit inherent to conventional delay-and-sum (DAS) beamforming. In the study, three typical algorithms—synthetic aperture (SA), apodized synthetic aperture (ASA), and sparse synthetic aperture (SSA)—are investigated in order to well achieve an effective imaging result for our newly derived circular array transducer with 55 MHz. In the scatterer’s simulation, at a depth of 1.5 mm, the ASA algorithm improves the lateral resolution from 260 μm for conventional SA to 175 μm (a 33% enhancement), while tripling the frame rate. Meanwhile, SSA maintains a resolution of 300 μm and reduces the data volume by 50%, laying the groundwork for real-time 3D imaging. Further phantom imaging testing shows that the SA algorithm has the best imaging effect on regional defects. The ASA algorithm has the best imaging effect on point defects while improving the imaging frame rate. This study provides insights and a foundation for optimizing circular-array intravascular ultrasound imaging, the proposed ASA model can be directly ported to existing 40–60 MHz circular-array IVUS systems, offering a new route for accurate early-plaque identification. Full article

Synthetic Aperture Radar (SAR) three-dimensional (3D) imaging has considerable potential in disaster monitoring and topographic mapping. Conventional 3D SAR imaging techniques for unmanned aerial vehicle (UAV) formations require rigorously regulated vertical or linear flight trajectories to maintain signal coherence. In practice, however, restricted collaboration precision among UAVs frequently prevents adherence to these trajectories, resulting in blurred scattering characteristics and degraded 3D localization accuracy. To address this, a 3D reconstruction technique based on horizontal-cross configurations is proposed, which establishes a new theoretical framework. This approach reduces stringent flight restrictions by transforming the requirement for vertical baselines into geometric flexibility in the horizontal plane. For dual-UAV subsystems, a geometric inversion algorithm is developed for initial scattering center localization. For multi-UAV systems, a multi-aspect fusion algorithm is proposed; it extends the dual-UAV inversion method and incorporates basis transformation theory to achieve coherent integration of multi-platform radar observations. Numerical simulations demonstrate an 80% reduction in implementation costs compared to tomographic SAR (TomoSAR), along with a 1.7-fold improvement in elevation resolution over conventional beamforming (CBF), confirming the framework’s effectiveness. This work presents a systematic horizontal-cross framework for SAR 3D reconstruction, offering a practical solution for UAV-based imaging in complex environments. Full article

In many monitoring scenarios, repeated and operator-independent assessments are needed. Wearable ultrasound technology has the potential to continuously provide the vital information traditionally obtained from conventional ultrasound scanners, such as in fetal monitoring for high-risk pregnancies. This work is an engineering study motivated by that setting. A 144-element annular capacitive micromachined ultrasonic transducer (CMUT) is hereby proposed for 3-D ultrasound imaging. The array is characterized by its compact size and cost-effectiveness, with a geometry and low-voltage operation that make it a candidate for future wearable integration. To enhance the imaging performance, we propose the utilization of a Fermat’s spiral virtual source (VS) pattern for diverging wave transmission and conduct a performance comparison with other VS patterns and standard techniques, such as focused and plane waves. To facilitate this analysis, a simplified and versatile simulation framework, enhanced by GPU acceleration, has been developed. The validation of the simulation framework aligned closely with expected values (0.002 ≤ MAE ≤ 0.089). VSs following a Fermat’s spiral led to a balanced outcome across metrics, outperforming focused wave transmissions for this specific aperture. The proposed transducer presents imaging limitations that could be improved in future developments, but it establishes a foundational framework for the design and fabrication of cost-effective, compact 2-D transducers suitable for 3-D ultrasound imaging, with potential for future integration into wearable devices. Full article

In the context of a medical imaging application for preclinical research, specifically cerebrovascular imaging in small animals, this work addresses the challenges associated with using a large-scale 2D ultrasonic array comprising $32\times 32$ elements ($96\lambda \times 96\lambda$ ). The application imposes stringent requirements: operation in the extreme near field, high spatial resolution, high frequency, high frame rate, and imaging within a highly attenuating medium. These demands, combined with current technological limitations, such as element size and constraints on the number of channels that can be driven in parallel, present significant challenges for system design and implementation. To assess system performance, plane wave imaging is employed as a reference modality due to its ability to meet high acquisition speed requirements. Our analysis reveals limitations in spatial coverage and image quality when operating the full aperture under plane wave transmission constraints. To address these limitations, we propose a sparse aperture strategy. When combined with advanced signal processing techniques, this approach enhances both contrast and resolution while preserving acquisition speed, making it a promising solution for high-performance ultrasonic imaging under the demanding conditions of preclinical research. Full article

Urban unmanned aerial vehicle (UAV) surveillance faces significant obstacles due to visual obstructions, inadequate lighting, small target dimensions, and acoustic signal interference caused by environmental noise and multipath propagation. To address these issues, this study proposes a multimodal detection framework that integrates an efficient YOLOv11-based visual detection module—trained on a comprehensive dataset containing over 50,000 UAV images—with a Capon beamforming-based acoustic imaging system using a 144-element spiral-arm microphone array. Adaptive compensation strategies are implemented to improve the robustness of each sensing modality, while detections results are validated through intersection-over-union and angular deviation metrics. The angular validation is accomplished by mapping acoustic direction-of-arrival estimations onto the camera image plane using established calibration parameters. Experimental evaluation reveals that the fusion system achieves outstanding performance under optimal conditions, exceeding 99% accuracy. However, its principal advantage becomes evident in challenging environments where individual modalities exhibit considerable limitations. The fusion approach demonstrates substantial performance improvements across three critical scenarios. In low-light conditions, the fusion system achieves 78% accuracy, significantly outperforming vision-only methods which attain only 25% accuracy. Under occlusion scenarios, the fusion system maintains 99% accuracy while vision-only performance drops dramatically to 9.75%, though acoustic-only detection remains highly effective at 99%. In multi-target detection scenarios, the fusion system reaches 96.8% accuracy, bridging the performance gap between vision-only systems at 99% and acoustic-only systems at 54%, where acoustic intensity variations limit detection capability. These experimental findings validate the effectiveness of the complementary fusion strategy and establish the system’s practical value for urban airspace monitoring applications. Full article

The rapid expansion of broadband wireless communication systems, including 5G, satellite networks, and next-generation IoT platforms, has created a strong demand for antenna architectures capable of real-time beam control, compact integration, and broad frequency coverage. Traditional reflectarrays, while effective for narrowband applications, often face inherent limitations such as fixed beam direction, high insertion loss, and complex phase-shifting networks, making them less viable for modern adaptive and reconfigurable systems. Addressing these challenges, this work presents a novel wideband planar metasurface that operates as a polarization rotation reflective metasurface (PRRM), combining 90° polarization conversion with 1-bit reconfigurable phase modulation. The metasurface employs a mirror-symmetric unit cell structure, incorporating a cross-shaped patch with fan-shaped stub loading and integrated PIN diodes, connected through vertical interconnect accesses (VIAs). This design enables stable binary phase control with minimal loss across a significantly wide frequency range. Full-wave electromagnetic simulations confirm that the proposed unit cell maintains consistent cross-polarized reflection performance and phase switching from 3.83 GHz to 15.06 GHz, achieving a remarkable fractional bandwidth of 118.89%. To verify its applicability, the full-wave simulation analysis of a 16 × 16 array was conducted, demonstrating dynamic two-dimensional beam steering up to ±60° and maintaining a 3 dB gain bandwidth of 55.3%. These results establish the metasurface’s suitability for advanced beamforming, making it a strong candidate for compact, electronically reconfigurable antennas in high-speed wireless communication, radar imaging, and sensing systems. Full article

Digital Beamforming SAR (DBF-SAR) provides high-resolution wide-swath imaging capability, yet it is affected by inter-channel amplitude, phase and time-delay errors induced by temperature variations and random error factors. Since all elevation channel data are weighted and summed by the DBF module in real time, conventional record-then-compensate approaches cannot meet real-time processing requirements. To resolve the problem, a real-time calibration architecture for Intermediate Frequency DBF (IFDBF) is presented in this paper. The Field-Programmable Gate Array (FPGA) implementation estimates amplitude errors through simple summation, time-delay errors via a simple counter, and phase errors via single-bin Discrete-Time Fourier Transform (DTFT). The time-delay and phase error information are converted into single-tone frequency components through Dechirp processing. The proposed method deliberately employs a reduced-length DTFT implementation to achieve enhanced delay estimation range adaptability. The method completes calibration within tens of PRIs (under 1 s). The proposed method is analyzed and validated through a spaceborne simulation and X-band 16-channel DBF-SAR experiments. Full article

In recent years, synthetic aperture radars (SARs) have been widely applied in various fields due to their all-weather, day-and-night global imaging capabilities. As one of the most common types of antennas, the reflector antenna offers some advantages for spaceborne radars, including low cost, lightweight, high gain, high radiation efficiency, and low sidelobes. Consequently, spaceborne SAR systems based on reflector antennas exhibit significant potential. This paper reviews the main types and characteristics of reflector antennas, with particular attention to the structural configurations and feed arrangements of deployable reflector antennas in spaceborne SAR applications. Additionally, some emerging techniques, such as digital beamforming, staggered SAR, and SweepSAR based on reflector antennas, are examined. Finally, future development directions in this field are discussed, including high-resolution wide-swath imaging and advanced antenna deployment schemes. Full article

A comprehensive framework for ultrasound imaging simulations is presented. Solutions to an inhomogeneous wave equation are provided, yielding a linear model for characterizing ultrasound propagation and scattering in soft tissue. This simulation approach, which is based upon the fast nearfield method, is implemented in the Fast Object-oriented C++ Ultrasound Simulator (FOCUS) and is extended to a range of imaging modalities, including synthetic aperture, B-mode, plane wave, and color Doppler imaging. The generation of radiofrequency (RF) data and the receive beamforming techniques employed for each imaging modality, along with background on color Doppler imaging, are described. Simulation results demonstrate rapid convergence and lower error rates compared to conventional spatial impulse response methods and Field II, resulting in substantial reductions in computation time. Notably, the framework effectively simulates hundreds of thousands of scatterers without the need for a full three-dimensional (3D) grid, and the inherent randomness in the scatterer distributions produces realistic speckle patterns. A plane wave imaging example, for instance, achieves high fidelity using 100,000 scatterers with five steering angles, and the simulation is completed on a personal computer in a few minutes. Furthermore, by modeling scatterers as moving particles, the simulation framework captures dynamic flow conditions in vascular phantoms for color Doppler imaging. These advances establish FOCUS as a robust, versatile tool for the rapid prototyping, validation, and optimization of both established and novel ultrasound imaging techniques. Full article

Millimeter-wave (mmWave) antennas and antenna arrays have gained significant attention due to their pivotal role in emerging wireless communication, sensing, and imaging technologies. With the rapid deployment of 5G and the transition toward 6G networks, the demand for compact, high-gain, and reconfigurable mmWave antennas has intensified. This article highlights recent advancements in mmWave antenna technologies, including hybrid beamforming using phased arrays, dynamic beam-steering enabled by liquid crystal and MEMS-based structures, and high-capacity MIMO architectures. We also examine the integration of metamaterials and metasurfaces for miniaturization and gain enhancement. Applications covered include wearable antennas with low-SAR textile substrates, conformal antennas for UAV-based mmWave relays, and high-resolution radar arrays for autonomous vehicles. The study further analyzes innovative fabrication methods such as inkjet and aerosol jet printing, micromachining, and laser direct structuring, along with advanced materials like Kapton, PDMS, and graphene. Numerical modeling techniques such as full-wave EM simulation and machine learning-based optimization are discussed alongside experimental validation approaches. Beyond communications, we assess mmWave systems for biomedical imaging, security screening, and industrial sensing. Key challenges addressed include efficiency degradation at high frequencies, interference mitigation in dense environments, and system-level integration. Finally, future directions, including AI-driven design automation, intelligent reconfigurable surfaces, and integration with quantum and terahertz technologies, are outlined. This comprehensive synthesis aims to serve as a valuable reference for advancing next-generation mmWave antenna systems. Full article