Search Results (1,859)

High-resolution TIR missions require sustained and well-characterized radiometric accuracy to support applications such as land surface temperature retrieval, drought monitoring, and surface energy budget analysis. To address this need, we develop an operational and automated ground-based vicarious radiometric calibration framework for TIR sensors and demonstrate its performance using the Wide-swath Thermal Infrared Imager (WTI) onboard Gaofen-5 01A (GF-5A). Three arid Gobi calibration sites were selected by integrating Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products, Shuttle Radar Topography Mission (SRTM)-derived topography, and WTI-based radiometric uniformity metrics to ensure low cloud cover, flat terrain, and high spatial homogeneity. Automated ground stations deployed at Golmud, Dachaidan, and Dunhuang have continuously recorded 1 min contact surface temperature since October 2023. Field-measured emissivity spectra, Integrated Global Radiosonde Archive (IGRA) radiosonde profiles, and MODTRAN (MODerate resolution atmospheric TRANsmission) v5.2 simulations were combined to compute top-of-atmosphere (TOA) radiances, which were subsequently collocated with WTI imagery. After data screening and gain-stratified regression, linear calibration coefficients were derived for each TIR band. Based on 189 scenes from February–July 2024, all four bands exhibit strong linearity (R-squared greater than 0.979). Validation using 45 independent scenes yields a mean brightness–temperature root-mean-square error (RMSE) of 0.67 K. A full radiometric-chain uncertainty budget—including contact temperature, emissivity, atmospheric profiles, and radiative transfer modeling—results in a combined standard uncertainty of 1.41 K. The proposed framework provides a low-maintenance, traceable, and high-frequency solution for the long-term on-orbit radiometric calibration of GF-5A WTI and establishes a reproducible pathway for future TIR missions requiring sustained calibration stability. Full article

This study focuses on the key challenges in detecting and estimating motion parameters of ground maneuvering targets for airborne radar sensors. The complex unknown motion states of the ground maneuvering target, including velocity, acceleration, and jerk, result in range migrations (RMs) and Doppler frequency migrations (DFMs). These effects severely degrade the long-time coherent accumulation performance of the airborne radar, thereby limiting the reliable detection and precise parameter estimation of maneuvering targets. To address this issue, a new detection and motion parameter estimation method based on the range frequency reversal transform (RFRT) and searching Lv’s distribution (SLVD), i.e., RFRT-SLVD, is proposed. Specifically, the third-order RM (TRM) and quadratic DFM (QDFM) are considered. The proposed method operates as follows: First, RMs are eliminated simultaneously via the RFRT operation, which multiplies the echo by its reversed data in the range frequency and slow-time domains, leveraging the symmetric equal-interval sampling property of the range frequency. Subsequently, a phase compensation function (PCF) related to the jerk is constructed to compensate the QDFM. Finally, the LVD is performed to remove residual DFMs and achieve effective signal energy accumulation. Additionally, the case of a fast-moving target with Doppler ambiguity is analyzed, and a method for estimating three motion parameters is provided. A key advantage of the proposed technique is its ability to directly compensate the RMs without requiring prior knowledge of the maneuvering target, while also avoiding the blind speed sidelobe (BSSL) effect. In comparison with existing algorithms, RFRT-SLVD achieves a balanced trade-off between parameter estimation performance and computational efficiency. Numerical analyses and experiments are conducted to validate the method, assessing its detection capability for ground maneuvering targets, Doppler ambiguity resolution in parameter estimation, computational complexity, and method applicability in multi-target scenarios. Full article

Cables in cable-supported bridges are critical structural components with exceptional tensile capacity, and their assessment is essential for the safety of both the cables themselves and the entire bridge. Microwave radar, a non-contact and efficient measurement technique, has emerged as a promising tool for bridge cable evaluation. This study demonstrates the deployment of microwave radar on bridge decks to efficiently measure the displacements of multiple cables, enabling coverage of all cables while effectively eliminating low-frequency components caused by deck deformation and radar motion using the LOWESS method. The measured cable displacements can be directly used to characterize vibrations, particularly for detecting vortex-induced vibrations (VIVs), without the need for numerical integration of accelerations. Furthermore, microwave radar is applied to free-decay testing for cable damping evaluation, providing an improved signal-to-noise ratio and eliminating the need for sensors installed via elevated platforms, thereby enhancing the reliability of damping assessments. The effectiveness of these approaches is validated through field testing on two cable-stayed bridges. Full article

In this work, different implementation strategies for a Time-Domain (TD) focusing procedure applied to airborne Synthetic Aperture Radar (SAR) raw data are presented, with the key objective of quantitatively assessing their computing time. In particular, two methodological approaches are proposed: a pixel-wise strategy, which processes each image pixel independently, and a matrix-wise strategy, which handles data blocks collectively. Both strategies are further extended to parallel execution frameworks to exploit multi-threading and multi-node capabilities. The presented analysis is conducted within the context of the airborne SAR infrastructure developed at the Institute for Electromagnetic Sensing of the Environment (IREA) of the National Research Council (CNR) in Naples, Italy. This infrastructure integrates an airborne SAR sensor and a high-performance Information Technology (IT) platform well-tailored to the parallel processing of huge amounts of data. Experimental results indicate an advantage of the pixel-wise strategy over the matrix-wise counterpart in terms of computing time. Furthermore, the adoption of parallel processing techniques yields substantial speedups, highlighting its relevance for time-critical SAR applications. These findings are particularly relevant in operational scenarios that demand a rapid data turnaround, such as near-real-time airborne monitoring in emergency response contexts. Full article

Over the past two decades, Light Detection and Ranging (LiDAR) technology has evolved from early National Aeronautics and Space Administration (NASA)-led airborne laser altimetry into commercially mature systems that now underpin vegetation remote sensing across scales. Continuous advancements in laser engineering, signal processing, and complementary technologies—such as Inertial Measurement Units (IMU) and Global Navigation Satellite Systems (GNSS)—have yielded compact, cost-effective, and highly sophisticated LiDAR sensors. Concurrently, innovations in carrier platforms, including uncrewed aerial systems (UAS), mobile laser scanning (MLS), Simultaneous Localization and Mapping (SLAM) frameworks, have expanded LiDAR’s observational capacity from plot- to global-scale applications in forestry, precision agriculture, ecological monitoring, Above Ground Biomass (AGB) modeling, and wildfire science. This review synthesizes LiDAR’s cross-domain capabilities for the following: (a) quantifying vegetation structure, function, and compositional dynamics; (b) recent sensor developments encompassing ALS discrete-return ${(ALS}_D)$, and ALS full-waveform $\left({ALS}_{FW}\right)$, photon-counting LiDAR (PCL), emerging multispectral LiDAR (MSL), and hyperspectral LiDAR (HSL) systems; and (c) state-of-the-art data processing and fusion workflows integrating optical and radar datasets. The synthesis demonstrates that many LiDAR-derived vegetation metrics are inherently transferable across domains when interpreted within a unified structural framework. The review further highlights the growing role of artificial-intelligence (AI)-driven approaches for segmentation, classification, and multitemporal analysis, enabling scalable assessments of vegetation dynamics at unprecedented spatial and temporal extents. By consolidating historical developments, current methodological advances, and emerging research directions, this review establishes a comprehensive state-of-the-art perspective on LiDAR’s transformative role and future potential in monitoring and modeling Earth’s vegetated ecosystems. Full article

To enhance the climate adaptability and diagnostic precision of university sustainability frameworks, this study presents a critical advancement to the PICSOU (Performance Indicators for Core Sustainability Objectives of Universities) framework’s Indoor Environmental Quality (IEQ) module. The research employs a comparative approach across two distinct climate zones: the campus of Chengdu Jincheng College in a humid subtropical climate (CDJCC; Köppen Cwa) with natural ventilation, and the campus of Tallinn University of Technology in a temperate climate (TalTech; Köppen Dfb) with mechanical ventilation. A key innovation at CDJCC was the deployment of a novel, integrated sensor that combines a Frequency-Modulated Continuous Wave (FMCW) radar module for real-time occupancy detection with standard IEQ sensor suite (CO₂, PM_2.5, temperature, humidity), enabling unprecedented analysis of occupant-IEQ dynamics. At TalTech, comprehensive IEQ monitoring was conducted using standard sensors. Results demonstrated that mechanical ventilation (TalTech) effectively decouples indoor conditions from external fluctuations. In contrast, natural ventilation (CDJCC) exhibits strong seasonal coupling, reflected by a Seasonal Ventilation Efficacy Coefficient (${\lambda }_{\mathit{season}}$), indicating that seasonal differences in effective ventilation are present but vary by indoor space type under occupied conditions. Consistent with this stronger indoor–outdoor linkage, PM_2.5 infiltration was also pronounced in naturally ventilated spaces, as evidenced by a high infiltration factor ($I/O$ ratio) that remained consistently elevated. This work conclusively validates a conditional, climate-resilient workflow for PICSOU’s IEQ category, integrating these empirical coefficients to transform its IEQ assessment into a dynamic and actionable tool for optimizing campus sustainability strategies globally. Full article

Scene depth information is a key component of any robotic mobile application. Range sensors, such as LiDAR, sonar, or radar, capture depth data of a scene. However, the data captured by these sensors frequently presents missing regions or information with a low confidence level. These missing regions in the depth data could be large areas without information, making it difficult to make decisions, for instance, for an autonomous vehicle. Recovering depth data has become a primary activity for computer vision applications. This work proposes and evaluates an interpolation model to infer dense depth maps from a Lab color space reference picture and an incomplete-depth image embedded in a completion pipeline. The complete proposal pipeline comprises convolutional layers and a convex combination of the infinity Laplacian and non-local means model. The proposed model infers dense depth maps by considering depth data and utilizing clues from a color picture of the scene, along with a metric for computing differences between two pixels. The work contributes (i) the convex combination of the two models to interpolate the data, and (ii) the proposal of a class of function suitable for balancing between different models. The obtained results show that the model outperforms similar models in the KITTI dataset and outperforms our previous implementation in the NYU_v2 dataset, dropping the MSE by 34.86%, 3.35%, and 34.42% for 4×, 8×, 16× upsampling tasks, respectively. Full article

Deep learning-based radar detection often suffers from poor cross-device generalization due to hardware heterogeneity. To address this, we propose a unified framework that combines rigorous calibration with adaptive temporal modeling. The method integrates three coordinated steps: (1) ensuring precise spatial alignment via improved Perspective-n-Point (PnP) calibration with closed-loop verification; (2) unifying signal statistics through multi-range bin calibration and chirp-wise Z-score standardization; and (3) enhancing feature consistency using a lightweight global–temporal adapter (GTA) driven by global gating and three-point attention. By combining signal-level standardization with feature-level adaptation, our framework achieves 86.32% average precision (AP) on the ROD2021 dataset. It outperforms the E-RODNet baseline by 22.88 percentage points with a 0.96% parameter increase, showing strong generalization across diverse radar platforms. Full article

This article reviews recent advances in microwave and radar techniques for non-contact measurement of human vital signs in dynamic conditions. The focus is on solutions that work when the subject is moving or performing everyday activities, rather than lying motionless in clinical settings. This review covers innovative biodegradable and flexible antenna designs for wearable devices operating in multiple frequency bands and supporting efficient 5G/IoT connectivity. Particular attention is paid to ultra-wideband (UWB) radar, Doppler sensors, and microwave reflectometry combined with advanced signal-processing and deep learning algorithms for robust estimation of respiration, heart rate, and other cardiopulmonary parameters in the presence of body motion. Applications in telemedicine, home monitoring, sports, and search and rescue are discussed, including localization of people trapped under rubble by detecting their vital sign signatures at a distance. This paper also highlights key challenges such as inter-subject anatomical variability, motion artifacts, hardware miniaturization, and energy efficiency, which still limit widespread deployment. Finally, related developments in microwave imaging and early detection of pathological tissue changes are briefly outlined, highlighting the shared components and processing methods. In general, microwave techniques show strong potential for unobtrusive, continuous, and environmentally sustainable monitoring of human physiological activity, supporting future healthcare and safety systems. Full article

The Fengyun-4 microwave satellite provides high-temporal-frequency observations at the 183 GHz band, providing unprecedented data for all-weather, three-dimensional measurements of atmospheric parameters. It is of importance to establish a simulated brightness temperature (BT) dataset for this band prior to launch, which can support the relevant quantitative applications significantly. Compared with clear-sky conditions, the accuracy of BT simulations under cloudy ones is considerably lower, primarily due to the influence of the adopted ice particle models. Up until now, few studies have systematically investigated ice particle model selection for different cloud types at the 183 GHz frequency band. In this paper, multi-sensor observations from Cloud Profiling Radar (CPR), Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), and Visible Infrared Imaging Radiometer Suite (VIIRS) were used as realistic atmospheric profiles. Using the high-precision radiative transfer model Atmospheric Radiative Transfer Simulator (ARTS), BT simulations at 183 GHz were performed to explore the optimal ice particle models for seven classical cloud types. The main conclusions are given as follows: (1) The sensitivity of simulated cloud radiances to ice particle habits differs with respect to different cloud phases. For altocumulus (Ac), stratocumulus (Sc), and cumulus (Cu) clouds, the different choices of ice particle model have little impacts on the simulated brightness temperatures (<1 K), with RMSEs below 3 K across multiple models, indicating that various models can be applied directly for such simulations. (2) For some mixed-phase clouds, including altostratus (As), nimbostratus (Ns), and deep convective (Dc) clouds, the Small Block Aggregate (SBA) and Small Plate Aggregate (SPA) models demonstrate good performance for As clouds, with RMSEs below 2.5 K, while the SBA, SPA, and Large Column Aggregate (LCA) models exhibit similarly good performance for Ns clouds, also achieving RMSEs below 2.5 K. For Dc clouds, although the SBA model yields RMSEs of approximately 10 K, it still provides a substantial improvement over the spherical model, whereas for cirrus (Ci) clouds, any non-spherical ice particle models are applicable, with RMSEs below 2 K. (3) Within the 183 GHz frequency band, channels with the higher weighting-function peaks are less sensitive to variable adoptions of ice particle models. These results offer valuable references for accurate radiative transfer simulations on 183 GHz frequency. Full article

Within the field of the structural monitoring of bridges, numerous technologies and methodologies have been developed. Among these, methods based on synthetic aperture radar (SAR) which utilise satellite data from missions such as Sentinel-1 (European Space Agency-ESA) and COSMO-SkyMed (Agenzia Spaziale Italiana—ASI) to capture displacements, temperature-related changes, and other geophysical measurements have gained increasing attention. However, SAR has yet to establish its value and potential fully; its broader adoption hinges on consistently demonstrating its robustness through recurrent applications, well-defined use cases, and effective strategies to address its inherent limitations. This study presents a systematic literature review (SLR) conducted in accordance with key stages of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 framework. An initial corpus of 1218 peer-reviewed articles was screened, and a final set of 25 studies was selected for in-depth analysis based on citation impact, keyword recurrence, and thematic relevance from the last five years. The review critically examines SAR-based techniques—including Differential Interferometric SAR (DInSAR), multi-temporal InSAR (MT-InSAR), and Persistent Scatterer Interferometry (PSI), as well as approaches to integrating SAR data with ground-based measurements and complementary digital models. Emphasis is placed on real-world case studies and persistent technical challenges, such as atmospheric artefacts, Line-of-Sight (LOS) geometry constraints, phase noise, ambiguities in displacement interpretation, and the translation of radar-derived deformations into actionable structural insights. The findings underscore SAR’s significant contribution to the structural health monitoring (SHM) of bridges, consistently delivering millimetre-level displacement accuracy and enabling engineering-relevant interpretations. While standalone SAR-based techniques offer wide-area monitoring capabilities, their full potential is realised only when integrated with complementary procedures such as thermal modelling, multi-sensor validation, and structural knowledge. Finally, this document highlights the persistent technical constraints of InSAR in bridge monitoring—including measurement ambiguities, SAR image acquisition limitations, and a lack of standardised, automated workflows—that continue to impede operational adoption but also point toward opportunities for methodological improvement. Full article

The malicious use of small unmanned aerial vehicles (UAVs) necessitates the development of effective countermeasures against such threats. Counter-UAV systems encompass detection, classification, and neutralization. Detection and classification can be performed using visual, audio, radar, or radio frequency (RF) sensors. This paper proposes a straightforward UAV classification scheme based on analyzing RF transmissions according to fundamental parameters such as bandwidth, duration, and center frequency. The statistics of these parameters are expected to be unique, enabling differentiation between various UAV models. The paper outlines the methodology for analyzing received waveforms to estimate the aforementioned parameters and their distributions. Computer vision tools are employed for spectrogram processing. The proposed approach is validated on a large dataset containing waveforms from eight UAV models. Three types of statistics are evaluated, demonstrating that each analyzed UAV exhibits distinct transmission-related features. Full article

The automatic emergency braking (AEB) system plays a crucial role in reducing rear-end collisions and is mandatory on certain heavy-duty vehicles, with future regulations extending to passenger cars. However, most current AEB systems are designed based on onboard sensors such as cameras and radar, which may fail to prevent collisions in scenarios where the lead vehicle is already in a collision. To address this issue, this study proposes an enhanced AEB control method based on Vehicle-to-Vehicle (V2V) communication and onboard sensors. The method utilizes V2V communication and onboard sensors to predict obstacles ahead, applying effective braking when necessary. Simulation results in Matlab/Simulink R2022a show that the proposed V2V-based AEB control method reduces the risk of chain collisions, ensuring that the ego vehicle can avoid rear-end collisions even when the lead vehicle is involved in a crash. Three simulation scenarios were designed, where both the subject vehicle and the lead vehicle travel at 120 km/h. The following three distances between the subject vehicle and the lead vehicle were considered: 45 m, 70 m, and 30 m. When the lead vehicle detects an obstacle 30 m ahead and suddenly applies emergency braking, the lead vehicle fails to avoid a collision. In this case, the subject vehicle, equipped only with onboard sensors, is also unable to successfully avoid the crash. However, when the subject vehicle is equipped with both onboard sensors and vehicle-to-vehicle communication, it can prevent a rear-end collision with the lead vehicle, maintaining a vehicle-to-vehicle distance of 1 m, 6.8 m, and 3.1 m, respectively, during the stopping process. This control method contributes to advancing the active safety technologies of autonomous vehicles. Full article

Airport airspace safety is increasingly threatened by small, unmanned aircraft systems and wildlife that traditional radar cannot detect. While earlier reviews addressed general counter-UAS techniques, individual sensors, or the detection of single objects such as birds or drones, none has systematically reviewed airport-based, multi-sensor surveillance strategies through a safety-theoretical lens. A systematic review, performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement, synthesized recent research on fixed, ground-based aerial detection capabilities for small aerial hazards, specifically unmanned aircraft systems (sUAS) and avian targets, within operational airport environments. Searches targeted English-language, peer-reviewed articles from 2016 through 2025 in Web of Science and Scopus. Due to methodological heterogeneity across sensor technologies, a narrative synthesis was executed. The review of thirty-six studies, analyzed through Reason’s Swiss Cheese Model and Endsley’s Situational Awareness framework, found that only layered multi-sensor fusion architectures effectively address detection gaps for Low-Slow-Small (LSS) threats. Based on these findings, the review proposes seamless integration with Air Traffic Management (ATM) and UAS Traffic Management (UTM) systems through standardized data-exchange interfaces, complemented by theoretically grounded risk-based deployment strategies aligning surveillance technology tiers with operational risk profiles, from basic Remote ID receivers in low-risk rural environments to comprehensive multi-sensor fusion at high-density hubs, major airports, and urban vertiports. Full article

The Synthetic Aperture Radar Interferometry (InSAR) technique enables researchers to generate Digital Elevation Models (DEMs) from SAR data, which researchers widely apply in multi-temporal analyses, including ground deformation monitoring, susceptibility mapping, and analysis of spatial changes in erosion basins. In this study, we generated two interferometric DEMs from Sentinel-1 (S1, VV polarization) and TerraSAR-X (TSX, HH polarization, ascending orbit) data, processed in SNAP, over a mountainous sector of the central Andes in Chile. We assessed the accuracy of the DEMs against two reference datasets: the SRTM DEM and a high-resolution LiDAR-derived DEM. We selected 150 randomly distributed points across different slope classes to compute statistical metrics, including RMSE and MedAE. Relative to the LiDAR DEM, both sensors yielded rMSE values of approximately 20 m, increasing to 23–24 m when compared with the SRTM DEM. The MedAE, a metric less sensitive to outliers, was 3.97 m for S1 and 3.26 m for TSX with respect to LiDAR, and 7.07 m for S1 and 7.49 m for TSX relative to SRTM. We observed a clear positive correlation between elevation error and terrain slope. In areas with slopes greater than 45°, the MedAE exceeded 14 m relative to the LiDAR DEM and reached ~15 m relative to the SRTM for both S1 and TSX. Full article