Search Results (1,899)

The accumulation of space debris in near-Earth orbit, particularly in Low Earth Orbit (LEO), poses an increasing threat to satellite operations, communication infrastructures, and long-term space sustainability. As modern constellations expand and incorporate advanced satellite technologies, including sensing and wireless communications, artificial intelligence-of-things (AIoT), enabled payloads, and edge computing for on-orbit data processing, the risk profile grows. This paper reviews the current debris environment and existing sensing and monitoring techniques, highlights major collision events and deliberate debris-generating activities, and analyzes the role of both governmental and commercial satellite constellations in exacerbating and mitigating the challenges. Emerging space surveillance and tracking (SST) techniques, leveraging radar, optical sensors, and interferometric SAR for enhanced intelligence, surveillance, and reconnaissance (ISR), are highlighted alongside software-defined networking (SDN) approaches and cloud communication technology that enable coordinated debris-avoidance maneuvers. Key international regulatory frameworks, tracking architectures, and mitigation measures, including alignment with ISO 24113 standards, advanced TT&C capabilities, and evolving active debris removal technologies, are examined. The study emphasizes the necessity of a global, interoperable ecosystem that integrates AI/ML (artificial intelligence and machine learning)-driven situational awareness, secure SATCOM links with AJ/LPI/LPD (anti-jamming/low probability of interception/low probability of detection) characteristics, and collaborative protocols among space agencies, commercial operators, and regulatory bodies to ensure the sustainable use of orbital space for future generations. Full article

Reliable estimation of rice yield is essential for food security planning, climate-resilient agriculture, and informed policy decisions. This review synthesizes recent research on the integration of remote sensing and crop simulation models for rice yield estimation. The analysis shows that optical and Synthetic Aperture Radar (SAR) data are the most commonly used remote sensing sources, with SAR proving especially valuable in monsoon-affected regions due to its ability to provide consistent observations under cloud cover. Among crop simulation models, DSSAT, APSIM, ORYZA, and WOFOST are most frequently applied, either independently or in combination with satellite-derived information. Across the reviewed studies, integrated approaches, particularly those using data assimilation and hybrid modeling, consistently achieve higher accuracy and better spatial representation of yield compared to standalone remote sensing or crop model methods. Despite these advances, limitations related to data availability, model calibration, scale mismatches, and climate-induced uncertainty remain significant. Based on the reviewed evidence, future efforts should focus on developing practical hybrid frameworks, improving multi-sensor data fusion, and designing scalable systems suited to data-limited regions. Overall, integrating remote sensing with crop simulation models offers a robust pathway for improving rice yield forecasting and supporting climate-adaptive agricultural management. Full article

A Digital Twin is a virtual environment that simulates, predicts, and optimizes the performance of its physical counterpart. Digital Twin models hold great potential in wireless networking testing and development. This paper aims to envision our concept of simulating the operation of different sensors in vehicle test-track conditions. Vehicle parameters are embedded into the edge computing entity, which uses them to generate a test configuration for the Digital Twin. This configuration is then applied in simulated sensor-output prediction, ultimately producing event data for the vehicle entity. The sensor suite—comprising radar, cameras, GPS and LiDAR—is modeled to provide the multi-modal input required for generating simulated perception data in the Digital Twin. To ensure realistic perception behavior, the physical vehicle is represented within a digital environment that reproduces the actual test track. This allows LiDAR occlusions to be attributed to genuine environmental structures (e.g., trees, buildings, other vehicles) rather than simulation artifacts. Within the Digital Twin, the objective is to evaluate how sensor signals—such as radar waves and LiDAR light pulses—propagate through the environment and how real-world obstacles may weaken or distort them. Historical datasets are used to calibrate and validate the Digital Twin, ensuring that the simulated sensor behavior aligns with real-world observations; the data collected during previous test runs can be used for visualization and analysis. Weather conditions are modeled to evaluate how rain, fog and snow impact sensor performance within the Digital Twin environment, to learn about the effects and predict sensor operation in different weather conditions. In this article, we examine the Digital Twin of our test track as a development environment for designing, deploying and testing ITS-enhanced road-weather services and warnings. These services integrate real-world road-weather observations, forecast data, roadside sensors and on-board vehicle measurements to support safe driving and optimize vehicle trajectories for both passenger and autonomous vehicles. This research is expected to benefit stakeholders involved in automotive testing, simulation and road-weather service development. Full article

Spinning tethered satellite systems represent a promising advancement in the design of spaceborne architectures for Earth and planetary observation. Leveraging the unique advantages of tether technology, such as mass efficiency in deploying large structures and fuel-free formation control, this study explores the feasibility and performance potential of CubeSat-scale spinning tethered formations. These systems consist of multiple spacecrafts connected by a tether, enabling easy dynamic adjustment of inter-satellite spacing and rotational velocity through conservation of angular momentum. Such flexibility facilitates precise, stable formations suitable for a range of remote sensing applications. In this paper, the authors present an overview of the dynamical modelling, deployment strategy, and operational advantages of spinning tether systems, focusing in particular on some key use cases: Earth, Moon and Mars surface observation. Three representative sensing modalities are analysed: (1) stereo imaging, where tethered platforms allow synchronized capture with tuneable baselines; (2) distributed radar sounding, which benefits from mechanically stabilized, spatially dispersed sensors to enhance resolution; and (3) Synthetic Aperture Radar (SAR) interferometry, where tether-induced baseline control improves accuracy and simplifies phase unwrapping. A performance assessment is provided for multiple orbital configurations around the Earth and the Moon. The results demonstrate that, while some issues still need to be explored in more detail, spinning tethered systems can offer competitive or superior observational performance in different mission scenarios compared to current technologies. The main challenges posed by this kind of architecture are discussed, alongside future research directions and development prospects. Full article

In complex maritime environments and scenarios with severe signal interference, unmanned surface vehicle (USV) swarms face dual challenges: unreliable GNSS signals due to interference and difficulties in accurately calibrating multi-sensor installation errors. These issues severely constrain the capability for high-precision cooperative formation operations. To address these problems, this paper proposes a cooperative localization and all-source online calibration algorithm based on a unified factor graph optimization framework. First, a tightly coupled all-source graph framework is established, integrating navigation radar, electro-optical systems (EOSs) with laser rangefinders, IMU, and GNSS into a sliding window. By leveraging high-precision mutual observations among the swarm, strong geometric constraints are constructed to mitigate the drift of individual inertial navigation systems. Second, an adaptive GNSS weighting mechanism based on signal quality and a degradation detection strategy based on eigenvalue analysis of the Fisher Information Matrix (FIM) are designed. These mechanisms enable online identification and robust estimation of extrinsic parameters, effectively resolving calibration divergence under weak excitation conditions such as straight-line sailing. Finally, the proposed algorithm is validated using field data from three USVs combined with simulated interference experiments. Results demonstrate that the algorithm can rapidly converge to high-precision calibration parameters without artificial targets (radar translation error < 0.2 m, EOS rotation error < 0.05°). During periods of simulated GNSS interference, the cooperative localization root mean square error (RMSE) is reduced to 2.85 m, representing an accuracy improvement of approximately 84.5% compared to traditional methods. This study achieves a “more accurate as it runs” cooperative navigation effect, providing reliable technical support for USV swarm applications in GNSS-denied environments. Full article

This dataset compiles continuous hydraulic and water quality observations from the combined sewer overflow structure at the outlet of the Graz-West R05 catchment in Austria, covering the period from 2008 to 2011. It integrates high-resolution in-sewer measurements of flow rate, water level, flow velocity and water quality parametres (COD, TSS, temperature), complemented by laboratory analyses of discrete grab samples. Water quality parametres were monitored using an in situ UV/VIS spectrometer installed on a floating pontoon. Additional locally calibrated COD values derived from laboratory measurements are included. The in-sewer data were acquired at 1 or 3 min intervals depending on flow conditions. Flow rates, water levels and overflow discharges were monitored using radar and ultrasonic sensors. Three nearby tipping-bucket rain gauges provided time-stamped precipitation increments, enabling the detailed reconstruction of wet-weather dynamics. A hydrodynamic SWMM model of the catchment, including geospatial information and dry-weather calibration, is included to support modelling applications. This combination of long-term measurements and a calibrated hydrodynamic model supports the development, testing and validation of process-based, statistical or data-driven approaches for simulating combined sewer system behaviour and pollutant dynamics. Full article

Accurate acquisition of low-observable targets with a minimal radar cross-section (RCS) poses a significant challenge for multi-source remote sensing systems, such as integrated radar–electro-optical (REO) platforms, particularly in complex electromagnetic environments characterized by strong noise interference and a high false-alarm rate. Conventional methods, which often treat data association and fusion from heterogeneous sensors as separate, offline processes, struggle with the dynamic uncertainties and real-time decision requirements of such scenarios. To address these limitations, this paper proposes a novel Evidence–Reinforcement Learning-based Decision and Control (ERL-DC) framework. It operates through a closed-loop architecture consisting of three core modules: A static assessment model for initial target prioritization, a Dempster–Shafer (D–S) evidence-based multi-source data decision generator for dynamic information fusion and uncertainty-aware target selection, and a Deep Reinforcement Learning (DRL) controller for noise-robust sensor steering. A high-fidelity simulation environment was developed to model the multi-source data stream, encompassing radar detection with clutter and false targets, as well as the physical constraints of the electro-optical (EO) servo system. Based on the averaged results from multiple Monte Carlo simulations, the proposed ERL-DC framework reduced the Average Decision Time (ADT) from 7.51 s to 4.53 s, corresponding to an absolute reduction of 2.98 s when compared to the conventional method integrating threshold logic with Model Predictive Control (MPC). Furthermore, the Net Discrimination Accuracy (NDA), derived from the statistical outcomes across all the simulation runs, exhibited an absolute increase of 37.8 percentage points, rising from 57.8% to 95.6%. These results indicate that ERL-DC achieves a more favorable trade-off in terms of scheduling efficiency, decision robustness, and resource utilization. The primary contribution is an intelligent, closed-loop architecture that tightly couples high-level evidential reasoning for multi-source data fusion with low-level adaptive control. Within the simulated environment characterized by clutter, false targets, and angular measurement noise, ERL-DC demonstrates improved target discrimination accuracy and decision efficiency compared to conventional methods. Future work will focus on online parameter adaptation and validation on physical platforms. Full article

This study examines residual low-order (linear and constant) phase errors in interferometric synthetic aperture radar (InSAR) when compact, high-frequency radar sensors are mounted on commercial uncrewed aerial vehicles (UAVs). Although higher carrier frequencies and shorter standoff ranges enable fine-resolution interferometry, the same characteristics—together with UAV platform instability—make the system highly vulnerable to motion-induced phase errors, which can significantly degrade or even invalidate DEM reconstruction. This paper first quantifies the admissible motion-error bounds for reliable multi-baseline phase-gradient estimation, and then introduces a post-autofocus correction scheme that estimates the residual linear term from the interferometric fringe frequency and refines it via an FFT-based correlation objective, while the constant term is calibrated using ground control points (GCPs). The method is validated through simulations of a 24 GHz UAV demonstrator. To the best of our knowledge, this work provides the first post-autofocus demonstration of linear-and-constant residual-error mitigation for UAV-based high-frequency multi-baseline InSAR. In the considered K-band setting, the proposed approach reduces the DEM error from 42 m to 0.2 m (≈98% improvement). Full article

Optical and synthetic aperture radar (SAR) image registration faces challenges from nonlinear radiometric distortions and geometric deformations caused by different imaging mechanisms. This paper proposes a coarse-to-fine registration algorithm integrating geographic information constraints with cross-modal feature consistency mapping. The coarse stage employs imaging geometry-based coordinate transformation with airborne navigation data to eliminate scale and rotation differences. The fine stage constructs a multi-scale phase congruency-based feature response aggregation model combined with rotation-invariant descriptors and global-to-local search for sub-pixel alignment. Experiments on integrated airborne optical/SAR datasets demonstrate superior performance with an average RMSE of 2.00 pixels, outperforming both traditional handcrafted methods (3MRS, OS-SIFT, POS-GIFT, GLS-MIFT) and state-of-the-art deep learning approaches (SuperGlue, LoFTR, ReDFeat, SAROptNet) while reducing execution time by 37.0% compared with the best-performing baseline. The proposed coarse registration also serves as an effective preprocessing module that improves SuperGlue’s matching rate by 167% and LoFTR’s by 109%, with a hybrid refinement strategy achieving 1.95 pixels RMSE. The method demonstrates robust performance under challenging conditions, enabling real-time UAV-based multi-sensor fusion applications. Full article

Safety in the operation of Unmanned Aerial Vehicles (UAVs) is emerging as an increasingly important requirement to avoid accidents or possible hazards, because of the growing number and variety of applications that make use of such systems. Consequently, the ability to detect and classify damages occurring on UAV components becomes critical, so that appropriate countermeasures can be applied on time. In this paper, a two-step methodology is proposed to detect damage to UAV propellers, and to classify its severity, so that the most appropriate response can be implemented. In fact, a first step is carried out onboard drone, in real-time, taking advantage of the acoustic emissions of the propeller and the potential of edge processing: a tiny Machine Learning (ML) classifier assesses the severity of the damage and, when deemed critical, the UAV is directed towards a ground station hosting a radar-based system, to discriminate the severity of the fault based on contactless vibration displacement and frequency measurements. The combination of both detection approaches realizes a diagnostic system that is time-responsive and accurate in defining the type, the amount, and the location of the damage. Damage classification performance values over 99% are provided by the embedded audio-based ML model; the radar-based step can further differentiate and measure the location of the propeller cut, which could eventually lead to forced landing of the UAV. Full article

A hybrid graphene-VO₂ reconfigurable terahertz metamaterial absorber is proposed for broadband radar cross-section (RCS) reduction and high-performance sensing. The designed structure leverages the phase transition property of VO₂ and the electrostatic tunability of graphene to achieve dynamic switching between ultra-broadband and narrowband absorption states. When VO₂ is in the metallic state and graphene is unbiased, the absorber exhibits over 90% absorption across 0.82~3.50 THz, corresponding to a relative bandwidth of 124%. In the narrowband mode, with VO₂ in the insulating state and graphene biased (E_f = 1 eV), a sharp absorption peak exceeding 60% is achieved at 1.48 THz. The symmetrical design ensures polarization insensitivity and wide-angle stability. Applications in broadband RCS reduction higher than 10 dB and refractive index sensing with a sensitivity of 24.86 GHz/RIU are demonstrated, surpassing conventional terahertz sensors. This work provides a promising platform for adaptive terahertz stealth and sensing systems. Full article

This study analyses the potential of a change detection method—the near-real-time cumulative sum change point detection method (CuSum-NRT)—applied to Sentinel-1 C-band synthetic aperture radar (SAR) data to monitor crops and identify field work based on a monthly number of changes. The temporal evolution of the number of changes occurring on Sentinel-1 backscatter at both VV and VH polarisations averaged at field scale was analysed over five years (2017–2021) and compared with NDVI derived from the Sentinel-2 multispectral instrument (MSI) sensor over more than 1000 fields in the southwest of France. The monthly number of changes detected did not show a significant difference between months with soil work and months with no soil work, so further analysis on the dates of changes should be conducted. The number of changes based on VV was found to poorly correlate with the VV backscatter (R_global = 0.25), and that based on VH was found to moderately correlate with the VH backscatter (R_global = 0.61): CuSum provides additional information compared to backscatter alone. The results also showed that the number of changes detected using CuSum-NRT is correlated to NDVI, mostly positively for the VH polarisation (R_max = 0.73, p-value < 0.05) and negatively for the VV polarisation (R_min = −0.69, p-value < 0.005). Furthermore, the analysis of crop groups (cereals, oilseeds, protein crops, fodder, vegetables, others) displayed statistically significant differences in terms of the annual number of changes occurring on both VH and VV polarisations, which has potential applications in crop classification. Full article

Road target detection is a crucial aspect of current research in automotive advanced driver assistance systems and intelligent transportation systems, where accuracy, speed, and lightweight design are key considerations. Compared to various sensors employed in driving assistance systems, millimeter-wave radar offers advantages such as all-weather operation, low hardware cost, strong penetration capability, and the ability to extract rich spatial information about targets. This paper tackles the challenges posed by the characteristics of Range-Angle map data from 77 GHz Frequency-Modulated Continuous Wave radar—namely, non-visible light imagery, abstract representation, rich fine details, and overlapping features. To this end, this paper proposes MMPFNet, a lightweight model based on the hypergraph mechanism with attention enhancement, as an extension of YOLOv13. First, an M-DSC3k2 module is proposed based on the hypergraph mechanism to enhance attention toward small targets. Second, a detection head with a double-bottleneck inverted MBConv-block structure is designed to improve the model’s accuracy and generalization capability. Third, a lightweight PPLConv module is customized to transform the backbone network, enhancing the model’s lightweight design while slightly reducing its accuracy. Considering the differences from traditional visible light datasets, the Focus Expansion-IoU loss function is introduced into the model to focus attention on different regression samples. The MMPFNet model achieves significant improvements in detecting common road targets such as pedestrians, bicycles, cars, and trucks on the Frequency-Modulated Continuous Wave radar Range-Angle dataset compared to the baseline YOLOv13n model: mAP50-95 increases by 16%, precision improves by 6%, and recall rises by 8.7%. MMPFNet is also evaluated on other non-visible light datasets such as CRUW-ONRD and soundprint datasets. Compared to commonly used detection models like FCOS and RetinaNet, MMPFNet achieves significant performance gains, attaining state-of-the-art results. Full article

Background: Falls are a leading cause of morbidity and mortality among older adults, creating a need for technologies that can automatically detect falls and summon timely assistance. The rapid evolution of sensor technologies and artificial intelligence has led to a proliferation of fall detection systems (FDS). This systematic review synthesizes the recent literature to provide a comprehensive overview of the current technological landscape. Objective: The objective of this review is to systematically analyze and synthesize the evidence from the academic literature on fall detection technologies. The review focuses on three primary areas: the sensor modalities used for data acquisition, the computational models employed for fall classification, and the emerging trend of shifting from reactive detection to proactive fall risk prediction. Methods: A systematic search of electronic databases was conducted for studies published between 2008 and 2025. Following the PRISMA guidelines, 130 studies met the inclusion criteria and were selected for analysis. Information regarding sensor technology, algorithm type, validation methods, and key performance outcomes was extracted and thematically synthesized. Results: The analysis identified three dominant categories of sensor technologies: wearable systems (primarily Inertial Measurement Units), ambient systems (including vision-based, radar, WiFi, and LiDAR), and hybrid systems that fuse multiple data sources. Computationally, the field has shown a progression from threshold-based algorithms to classical machine learning and is now dominated by deep learning architectures, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers. Many studies report high performance, with accuracy, sensitivity, and specificity often exceeding 95%. An important trend is the expansion of research from post-fall detection to proactive fall risk assessment and pre-impact fall prediction, which aim to prevent falls before they cause injury. Conclusions: The technological capabilities for fall detection are well-developed, with deep learning models and a variety of sensor modalities demonstrating high accuracy in controlled settings. However, a critical gap remains; our analysis reveals that 98.5% of studies rely on simulated falls, with only two studies validating against real-world, unanticipated falls in the target demographic. Future research should prioritize real-world validation, address practical implementation challenges such as energy efficiency and user acceptance, and advance the development of integrated, multi-modal systems for effective fall risk management. Full article

Target decomposition is an essential method for the interpretation of polarimetric Synthetic Aperture Radar (SAR). Most current polarimetric target decomposition methods are designed for quad-pol SAR data, while there is a scarcity of methods tailored for dual-pol SAR data, and these methods often struggle to accurately capture the complete scattering components of targets. Compared to quad-pol SAR, space-borne SAR systems more frequently acquire dual-pol SAR data, which offers a wider observation swath and higher resolution. The fast generalized polarimetric target decomposition (FGPTD) method has exhibited excellent target decomposition performance for quad-pol SAR data by searching for the optimal scattering models through nonlinear optimization. To address the core problem of inaccurate scattering component extraction in dual-pol SAR, deep learning is adopted to simulate the nonlinear optimization process of the FGPTD method. Its powerful nonlinear mapping capability enables the model to learn the intrinsic correlation between dual-pol SAR data and the complete scattering components obtained by FGPTD. Therefore, this paper proposes a model and learning-aided target decomposition method for dual-pol SAR. Firstly, FGPTD is performed on existing quad-pol SAR data. Subsequently, a mapping set between dual-pol SAR data and scattering components is constructed. Then, a neural network that integrates residual connections and dilated convolutional kernels is trained using the constructed mapping set. Finally, the well-trained neural network is tested on dual-pol SAR data from other regions and other sensors. Experimental results demonstrate that the proposed method’s target decomposition results are close to those of quad-pol target decomposition and superior to current state-of-the-art dual-pol target decomposition methods. Full article