Search Results (562)

Unmanned aerial vehicles (UAVs) have emerged as a promising platform for integrated sensing and communication (ISAC) due to their mobility and deployment flexibility. By adaptively adjusting their flight trajectories, UAVs can maintain favorable line-of-sight (LoS) communication links and sensing angles, thus enhancing overall system performance in dynamic and complex environments. However, ensuring physical layer security (PLS) in such UAV-assisted ISAC systems remains a significant challenge, particularly in the presence of mobile users and potential eavesdroppers. This manuscript proposes a joint optimization framework that simultaneously designs robust transmit beamforming and UAV trajectories to secure downlink communication for multiple ground users. At each time slot, the UAV predicts user positions and maximizes the secrecy sum-rate, subject to constraints on total transmit power, multi-target sensing quality, and UAV mobility. To tackle this non-convex problem, we develop an efficient optimization algorithm based on successive convex approximation (SCA) and constrained optimization by linear approximations (COBYLA). Numerical simulations validate that the proposed framework effectively enhances the secrecy performance while maintaining high-quality sensing, achieving near-optimal performance under realistic system constraints. Full article

Urban environments present substantial obstacles to GPS positioning accuracy, primarily due to multipath interference and limited satellite visibility. To address these challenges, we propose a novel weighting approach, referred to as the HK model, that enhances real-time GPS positioning performance by leveraging the variability of the signal-to-noise ratio (SNR), without requiring auxiliary sensors. Analysis of 24 h observational datasets collected across diverse environments, including open-sky (OS), city streets (CS), and urban canyons (UC), demonstrates that multipath-affected non-line-of-sight (NLOS) signals exhibit significantly greater SNR variability than direct line-of-sight (LOS) signals. The HK model classifies received signals based on the standard deviation of their SNR and assigns corresponding weights during position estimation. Comparative performance evaluation indicates that relative to existing weighting models, the HK model improves 3D positioning accuracy by up to 22.4 m in urban canyon scenarios, reducing horizontal RMSE from 13.0 m to 4.7 m and vertical RMSE from 19.5 m to 6.9 m. In city street environments, horizontal RMSE is reduced from 11.6 m to 3.8 m. Furthermore, a time-sequential analysis at the TEHE site confirms consistent improvements in vertical positioning accuracy across all 24-hourly datasets, and in terms of horizontal accuracy, in 22 out of 24 cases. These results demonstrate that the HK model substantially surpasses conventional SNR- or elevation-based weighting techniques, particularly under severe multipath conditions frequently encountered in dense urban settings. Full article

In the field of high-altitude operations, the frequent occurrence of fall accidents is usually closely related to safety measures such as the incorrect use of safety locks and the wrong installation of safety belts. At present, the manual inspection method cannot achieve real-time monitoring of the safety status of the operators and is prone to serious consequences due to human negligence. This paper designs a new type of high-altitude operation safety device based on the STM32F103 microcontroller. This device integrates ultra-wideband (UWB) ranging technology, thin-film piezoresistive stress sensors, Beidou positioning, intelligent voice alarm, and intelligent safety lock. By fusing five modes, it realizes the functions of safety status detection and precise positioning. It can provide precise geographical coordinate positioning and vertical ground distance for the workers, ensuring the safety and standardization of the operation process. This safety device adopts multi-modal fusion high-altitude operation safety monitoring technology. The UWB module adopts a bidirectional ranging algorithm to achieve centimeter-level ranging accuracy. It can accurately determine dangerous heights of 2 m or more even in non-line-of-sight environments. The vertical ranging upper limit can reach 50 m, which can meet the maintenance height requirements of most transmission and distribution line towers. It uses a silicon carbide MEMS piezoresistive sensor innovatively, which is sensitive to stress detection and resistant to high temperatures and radiation. It builds a Beidou and Bluetooth cooperative positioning system, which can achieve centimeter-level positioning accuracy and an identification accuracy rate of over 99%. It can maintain meter-level positioning accuracy of geographical coordinates in complex environments. The development of this safety device can build a comprehensive and intelligent safety protection barrier for workers engaged in high-altitude operations. Full article

Localization is one of the essential problems in the Internet of Things (IoT). Dynamic changes in the radio environment may lead to poor localization accuracy or discontinuous localization in non-line-of-sight (NLOS) scenarios. To address this problem, this paper proposes a localization enhancement method based on direct-path identification and tracking. More specifically, the proposed method significantly reduces the range error and localization error by quickly identifying the line-of-sight (LOS) to NLOS transition and effectively tracking the direct path. In a large testing hall, localization experiments based on the ultra-wideband (UWB) signal have been carried out. Experimental results show that the proposed method achieves a root mean square localization error of less than 0.3 m along the user equipment (UE) movement trajectory with serious NLOS propagation conditions. Compared with conventional methods, the proposed method significantly improves localization accuracy while ensuring continuous localization. Full article

The evolution of 5G technology has led to significant advancements in high-accuracy positioning. However, the actual performance of 5G signals for user equipment (UE) positioning has not been thoroughly examined, especially under varying propagation conditions. This research presents a comprehensive evaluation of 5G positioning using both uplink sounding reference signals (UL-SRS) and downlink positioning reference signals (DL-PRS) under line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. In the uplink scenario, the UE transmits SRS signals to the gNBs, enabling precise localization. In the downlink scenario, the gNBs transmit PRS signals to the UE for accurate position estimation. Expanding beyond LOS environments, this study explores the challenges posed by NLOS conditions and analyzes their impact on positioning accuracy. Through a comparative analysis of UL-SRS and DL-PRS signals, this study enhances the current understanding of 5G positioning performance, offering empirical insights and quantitative benchmarks that serve as a guide for the development of more precise localization methods. The simulation results show that DL-PRS achieves high accuracy in LOS conditions, while UL-SRS performs well for UE positioning under NLOS conditions in urban environments. Full article

Ultraviolet (UV) communication, characterized by non-line-of-sight (NLOS) scattering, holds substantial potential for enabling communication networking in unmanned aerial vehicle (UAV) formations within strong electromagnetic interference environments. This paper proposes a networking protocol for large-scale mobile ultraviolet communication networks (LSM-UVCN). In large-scale networks, the proposed protocol establishes multiple non-interfering transmission paths based on a connection matrix simultaneously, ensuring reliable space division multiplexing (SDM) and optimizing the utilization of network channel resources. To address frequent network topology changes in mobile scenarios, the protocol employs periodic maintenance of the connection matrix, significantly reducing the adverse impacts of node mobility on network performance. Simulation results demonstrate that the proposed protocol achieves superior performance in large-scale mobile UV communication networks. By dynamically adjusting the connection matrix update frequency, it adapts to varying node mobility intensities, effectively minimizing control overhead and data loss rates while enhancing network throughput. This work underscores the protocol’s adaptability to dynamic network environments, providing a robust solution for high-reliability communication requirements in complex electromagnetic scenarios, particularly for UAV swarm applications. The integration of SDM and adaptive matrix maintenance highlights its scalability and efficiency, positioning it as a viable technology for next-generation wireless communication systems in challenging operational conditions. Full article

Intelligent reflecting surface (IRS) has emerged as a promising solution to establish propagation paths in non-line-of-sight (NLoS) scenarios, effectively mitigating blockage challenges in direct vehicle-to-infrastructure (V2I) links. This study investigates a time-varying multi-IRS-assisted multiple-input multiple-output (MIMO) communication system, aiming to maximize the system sum rate through the joint optimization of base station (BS) precoding and IRS phase configurations. The formulated problem exhibits inherent non-convexity and time-varying characteristics, posing significant optimization challenges. To address these, we propose a low-complexity dimension-wise sine maximization (DSM) algorithm, grounded in the sum path gain maximization (SPGM) criterion, to efficiently optimize the IRS phase shift matrix. Concurrently, the water-filling (WF) algorithm is employed for BS precoding design. Simulation results demonstrate that compared with traditional methods, the proposed DSM algorithm achieves a 14.9% increase in sum rate, while exhibiting lower complexity and faster convergence. Furthermore, the proposed multi-IRS design yields an 8.7% performance gain over the single-IRS design. Full article

To address the limitations of single-modality UWB/IMU systems in complex indoor environments, this study proposes a multimodal fusion localization method based on xLSTM. After extracting features from UWB and IMU data, the xLSTM network enables deep temporal feature learning. A three-stage residual fusion module is introduced to enhance cross-modal complementarity, while a multi-head attention mechanism dynamically adjusts the sensor weights. The end-to-end trained network effectively constructs nonlinear multimodal mappings for two-dimensional position estimation under both static and dynamic non-line-of-sight (NLOS) conditions with human-induced interference. Experimental results demonstrate that the localization errors reach 0.181 m under static NLOS and 0.187 m under dynamic NLOS, substantially outperforming traditional filtering-based approaches. The proposed deep fusion framework significantly improves localization reliability under occlusion and offers an innovative solution for high-precision indoor positioning. Full article

Reconfigurable Intelligent Surfaces (RISs) are among the key technologies envisaged for sixth-generation (6G) multiple-input multiple-output (MIMO)–orthogonal frequency division multiplexing (OFDM) wireless systems. However, their passive nature and the frequent absence of a line-of-sight (LoS) path in dense urban environments make uplink channel estimation and localization challenging tasks. Therefore, to achieve channel estimation and localization, this study models the RIS-mobile station (MS) channel as a double-sparse angular structure and proposes a hybrid channel parameter estimation framework for RIS-assisted MIMO-OFDM systems. In the hybrid framework, Simultaneous Orthogonal Matching Pursuit (SOMP) first estimates coarse angular supports. The coarse estimates are refined by a novel refinement stage employing a Variational Bayesian Expectation Maximization (VBEM)-based Off-Grid Sparse Bayesian Learning (OG-SBL) algorithm, which jointly updates azimuth and elevation offsets via Newton-style iterations. An Angle of Arrival (AoA)–Angle of Departure (AoD) matching algorithm is introduced to associate angular components, followed by a 3D localization procedure based on non-LoS (NLoS) multipath geometry. Simulation results show that the proposed framework achieves high angular resolution; high localization accuracy, with 97% of the results within 0.01 m; and a channel estimation error of 0.0046% at 40 dB signal-to-noise ratio (SNR). Full article

This study addresses the ’last-mile’ transportation challenges that arise in steep and narrow forest terrain by proposing a novel robotic palanquin system called roCaGo. It is inspired by the mechanical principles of two-wheel-steering and two-wheel-drive (2WS/2WD) bicycles. The roCaGo system integrates front- and rear-wheel-drive mechanisms, as well as a central suspension structure for carrying loads. Unlike conventional forestry machinery, which requires wide, well-maintained roads or permanent rail systems, the roCaGo system enables flexible, operator-assisted transport along narrow, unprepared mountain paths. A dynamic model of the system was developed to design a stabilization control strategy, enabling roCaGo to maintain transport stability and assist the operator during navigation. Numerical simulations and preliminary physical experiments demonstrate its effectiveness in challenging forest environments. Furthermore, the applicability of roCaGo has been extended to include use as a mobile third-person viewpoint platform to support the remote operation of existing forestry equipment; specifically the LV800crawler vehicle equipped with a front-mounted mulcher. Field tests involving LiDAR sensors mounted on roCaGo were conducted to verify its ability to capture the environmental data necessary for non-line-of-sight teleoperation. The results show that roCaGo is a promising solution for improving labor efficiency and ensuring operator safety in forest logistics and remote-controlled forestry operations. Full article

This study presents an integrated smart construction monitoring system that combines point cloud data (PCD) from a 3D laser scanner with real-time IoT sensors and ultra-wideband (UWB) indoor positioning technology to enhance construction site safety and quality management. The system addresses the limitations of traditional BIM-based methods by leveraging high-precision PCD that accurately reflects actual site conditions. Field validation was conducted over 17 days at a residential construction site, focusing on two floors during concrete pouring. The concrete strength prediction model, based on the ASTM C1074 maturity method, achieved prediction accuracy within 1–2 MPa of measured values (e.g., predicted: 26.2 MPa vs. actual: 25.3 MPa at 14 days). The UWB-based worker localization system demonstrated a maximum positioning error of 1.44 m with 1 s update intervals, enabling real-time tracking of worker movements. Static accuracy tests showed localization errors of 0.80–0.94 m under clear line-of-sight and 1.14–1.26 m under partial non-line-of-sight. The integrated platform successfully combined PCD visualization with real-time sensor data, allowing construction managers to monitor concrete curing progress and worker safety simultaneously. Full article

Requirements for Unmanned Aerial Vehicle (UAV) applications in low-altitude operations are escalating, which demands resilient Position, Navigation and Timing (PNT) solutions incorporating global navigation satellite system (GNSS) services. However, UAVs often operate in stringent environments with degraded GNSS performance. Practical challenges often arise from dense, dynamic, complex, and uncertain obstacles. When flying in complex environments, it is important to consider signal degradation caused by reflections (multipath) and obscuration (Non-Line of Sight (NLOS)), which can lead to positioning errors that must be minimized to ensure mission reliability. Recent works integrate GNSS reliability maps derived from pseudorange error estimations into path planning to reduce loss-of-GNSS risks with PNT degradations. To accommodate multiple constraint conditions attempting to improve flight resilience against GNSS-degraded environments, this paper proposes a reinforcement learning (RL) approach to feature GNSS signal quality awareness during path planning. The non-linear relations between GNSS signal quality in the form of dilution of precision (DoP), geographic locations, and the policy of searching sub-minima points are learned by the clipped Proximal Policy Optimization (PPO) method. Other constraints considered include static obstacle occurrence, altitude boundary, forbidden flying regions, and operational volumes. The reward and punishment functions and the training method are designed to maximize the success criteria of approaching destinations. The proposed RL approach is demonstrated using a real 3D map of Indianapolis, USA, in the Godot engine, incorporating forecasted DoP data generated by a Geospatial Augmentation system named GNSS Foresight from Spirent. Results indicate a 36% enhancement in mission success rates when GNSS performance is included in the path planning training. Additionally, the varying tensor size, representing the UAV’s DoP perception range, exhibits a positive proportion relation to a higher mission rate, despite an increment in computational complexity. Full article

Commonly, accelerometers are used to determine the tension force in cables through an indirect process; however, it is necessary to know the mechanical parameters of each element, such as mass and length. Typically, obtaining or measuring these parameters is not feasible. Therefore, this research proposed an alternative methodology to indirectly estimate them based on historical information about the so-called classic instruments (accelerometers and hydraulic jack). This case study focused on the Rio Papaloapan Bridge located in Veracruz, Mexico, a structure that has experienced material casting issues due to inadequate heat treatment in some cable top anchor over its lifespan. Thirteen cables from the structure were selected to evaluate the proposed methodology, yielding results within 3.8% of difference compared to direct tension estimation generated by a hydraulic jack. Furthermore, to enhance data collection, this process was complemented using a computer vision methodology. This involved remotely measuring the vibration frequency of cables from high-resolution videos recorded with a smartphone. The non-contact method was validated in a laboratory using a vibrating table, successfully estimating oscillation frequencies from video-recording with a fixed camera. A field test on eight cables of a bridge was also conducted to assess the performance and feasibility of the proposed method. The results demonstrated an RMS Error of approximately 2 mHz and a percentage difference in the tension force estimation below 3% compared to an accelerometer measurement approach. Finally, it was determined that this composed methodology for indirect tension force determination is a viable option when: (1) cables are challenging to access; (2) there is no line of sight between the camera and cables outside the bridge; (3) there is a lack of information about the mechanical parameters of the cables. Full article

Horizontal dilution of precision (HDOP) is a widely used quality indicator of Global Navigation Satellite System (GNSS) positioning, considering only satellite geometry. In this study, HDOP was simulated using GNSS almanacs and high-resolution digital surface models (DSMs) along three European road sections: Oslo— Svinesund Bridge (Norway); Hamburg city center (Germany); and Rotterdam—Dutch–German border (Netherlands). This study was accomplished as part of the MODI project, which is a cross-border initiative to accelerate Cooperative, Connected, and Automated Mobility (CCAM). Our analysis revealed excellent or good overall GNSS performance in the study areas, particularly on highway sections with 99–100% of study points having a median HDOP that is categorized as excellent (HDOP < 2) or good (HDOP < 5). However, the road section in Hamburg’s city center presents challenges. When GPS is used alone, 8% of the study points experience weak or poor HDOP, and there are study points where the system is available (HDOP < 5) less than 50% of the time. Combining GNSS constellations significantly improved system availability, reaching 95% for 99% of the study points in Hamburg. To validate our simulations, we compared results with GNSS observations from a survey vehicle in Hamburg. Initial low correlation was attributed to the reception of signals from non-line-of-sight satellites. By excluding satellites with low signal-to-noise ratios, the correlation increased significantly, and reasonable agreement was obtained. We also examined the impact of using a 10 m DSM instead of a 1 m DSM in Hamburg. While the coarser spatial resolution offers computational benefits, it may miss critical details for accurate assessment of satellite visibility. Full article

In this paper, we investigate a simultaneous wireless information and power transfer (SWIPT) communication system enhanced by an intelligent reflecting surface (IRS). Our study takes into account the imperfections in the phase shift of the IRS and the presence of interfering signals reflected by the IRS at the destination terminal. Additionally, our analysis incorporates both the presence of a line-of-sight path between the source and destination and a non-linear energy-harvesting model. In order to assess the influence of phase error and interference on the considered system, closed-form and asymptotic expression for the system’s outage probability, ergodic capacity, and energy efficiency (EE) are derived. Simulation results are presented to corroborate our analysis and illustrate the impact of phase error, interference, the number of reflecting elements, and various system parameters on the system performance. Full article