Search Results (711)

Wireless sensing networks increasingly extend into obstacle-prone deployments, where physical blockage degrades reliability and open propagation exposes transmission activity. Intelligent reflecting surfaces (IRSs) establish programmable paths around obstacles while passive elements remain constrained by severe cascaded attenuation. To address the tradeoff between reliability and covertness, we propose an unmanned aerial vehicle (UAV) -assisted active-IRS architecture under probabilistic line-of-sight and non-line-of-sight propagation conditions that accounts for direct leakage from the transmitter to the warden together with residual jammer cancellation and always-on IRS circuit noise under a finite output power budget. Furthermore, bidirectional Kullback–Leibler analysis identifies the reverse divergence as the tighter restriction and converts the covertness requirement into conservative gain bounds under warden location uncertainty and relative phase uncertainty conditions between the direct and aggregate reflected fields. Subsequently, closed-form phase control for calibrated equal-gain elements and gain monotonicity reduce the joint design to an exhaustive search over the prescribed placement grid. The numerical results demonstrate a SINR advantage over passive reflection and single-element relaying across the evaluated settings. The finite-array and hardware analyses show that gain back-off enforces a prescribed covert-outage limit while direct leakage and residual self-interference remain explicitly controlled. Overall, the framework provides a transparent basis for reliable covert sensing through UAV-assisted active reflection. Full article

In Global Navigation Satellite System (GNSS)-denied indoor environments, ultra-wideband (UWB) localization of unmanned ground vehicles (UGVs) is challenged by position-dependent anchor visibility and mixed line-of-sight (LOS)/non-line-of-sight (NLOS) ranging. This study proposes a soft continuous confidence weighting method within an adaptive Kalman filter (AKF)-based UWB/inertial measurement unit (IMU) localization framework. The vehicle model uses motor pulse increments and IMU yaw-rate measurements as inputs and outputs vehicle position and heading estimates. Virtual forward–backward iteration converts inconsistencies between the current UWB ranges and tag–anchor geometry into terminal virtual-anchor displacements. A half-Gaussian function then maps each displacement to a continuous confidence coefficient. The resulting coefficients are incorporated into weighted least-squares (WLS) and AKF localization, while the UWB measurement-noise covariance is adaptively updated using the range innovations. The proposed method was evaluated through static calibration and dynamic localization experiments. These experiments compared soft and hard weighting schemes and assessed the contribution of AKF fusion. These results indicate that the method proposed in this study improves localization accuracy, robustness, and temporal continuity under position-dependent anchor visibility and mixed LOS/NLOS conditions. Full article

Accurate channel estimation in millimeter-wave MIMO-OFDM systems is hindered by limited pilot resources and the high sensitivity of physical priors to propagation conditions. This paper proposes PERL, a physics-enhanced residual learning framework that integrates ray-tracing (RT) priors with deep neural networks to refine coarse RT-based estimates. Unlike direct channel reconstruction, PERL learns a residual correction atop the RT baseline, with the correction magnitude adaptively gated according to noise level and RT reliability, thereby relying more on physical priors under low SNR and exploiting pilot observations for refinement at high SNR. The framework leverages a Transformer-based multimodal attention mechanism to deeply fuse sparse pilot observations, path-level propagation features (delay, angle, power, and phase), and scene-level statistical descriptors, enabling physical constraints and data-driven refinement to interact effectively. Experiments on a 28 GHz urban macro-cellular MIMO-OFDM scenario demonstrate that PERL achieves an overall NMSE of −28.82 dB, outperforming the RT baseline by 5.30 dB and the MMSE estimator by over 20 dB, with notably larger gains under non-line-of-sight conditions where the RT prior is less accurate. Link-level evaluations further confirm improved error vector magnitude and maintained bit/block error rates relative to the RT baseline, validating that the proposed physical-data collaborative paradigm not only enhances estimation accuracy but also preserves communication reliability, while offering a promising foundation for future detection-aware and integrated-sensing-and-communication optimizations. Full article

Indoor positioning using commodity Wi-Fi infrastructure is attractive for smart buildings and Internet of Things applications, but the practical accuracy of Wi-Fi Fine Timing Measurement (FTM) remains limited by non-line-of-sight propagation, multipath delay, access-point-dependent ranging bias, and unstable anchor geometry. This paper proposes a measurement-reliability learning and geometry-constrained fusion framework, termed MRL-GCF, for robust horizontal Wi-Fi FTM indoor localization. MRL-GCF learns the reliability of each access-point observation from a multi-factor representation that includes Received Signal Strength Indicator (RSSI), logarithmic FTM range, short-window range stability, RSSI fluctuation, access-point visibility, abnormal-range tendency, and coarse anchor geometry. A lightweight heteroscedastic neural calibrator estimates both range bias and observation uncertainty. A supervised reliability-regime head is further trained from residual-regime soft targets, and its entropy is used as a propagation-ambiguity measure. The learned uncertainty is fused with propagation ambiguity, map obstruction, material-aware obstruction cues, and anchor geometry to select reliable anchors and construct a trust-weighted nonlinear least-squares localization objective. To avoid overestimating performance from repeated scans at identical survey points, both scan-level and point-held-out protocols were adopted. Experiments were conducted in a lobby, a classroom, and a dormitory using 4410 synchronized RSSI-FTM scans. On 882 scan-level test queries, MRL-GCF achieved mean absolute errors of 0.88 m, 0.55 m, and 1.20 m, with sub-3 m success rates of 98.0%, 99.0%, and 96.5%, respectively. Additional replay-based dynamic, temporal, cross-device, AP-density, uncertainty-calibration, map-availability, and coefficient-sensitivity analyses were included to examine deployment-oriented robustness. These results indicate that learning measurement reliability while preserving geometric constraints provides a practical and interpretable solution for robust Wi-Fi FTM indoor positioning. Full article

Efficient robotic teleoperation for industrial inspection requires interfaces that maximize intuitive control and situational awareness. While traditional mixed reality (MR) systems offer alternatives, standard controller-based methods lack environment customization and demand external peripheral hardware. This study introduces a peripheral-hardware-free, customizable 3D spatial virtual cockpit application deployed on a Meta Quest 3 headset for long-distance, non-line-of-sight teleoperation of the Improbability Roller-2, a mobile robotic platform that dynamically adjusts its wheel geometry to traverse varied terrain over a Virtual Private Network (VPN). The system’s novel spatial cockpit architecture allows operators to scale multi-channel parameters dynamically without relying on physical hardware controllers. The framework was evaluated using transmission-quality benchmarks, an active industrial machine shop deployment, and a 20-participant user study assessing usability and cognitive load. Experimental results yielded a mean System Usability Scale (SUS) score of 82.75, corresponding to an excellent usability rating, and a low mean operator workload, with a NASA Task Load Index (NASA-TLX) score of 5.19 out of 21. Participants also rated the workspace customization feature positively, assigning it a rating of 4.6 out of 5. In terms of communication performance, the system successfully completed all inspection tasks even under severely degraded VPN conditions. These findings demonstrate that the proposed customizable 3D spatial interface provides a robust, scalable alternative to traditional controller-based setups for long-distance remote robotic inspection in real-world industrial settings. Full article

The rapid growth of computing-intensive and latency-sensitive applications, including augmented reality, virtual reality, and self-driving systems, has increased the demand for low-latency and energy-efficient processing solutions. Mobile edge computing (MEC) has evolved as a transformative paradigm by relocating computation to the network edge, closer to end users. Unmanned aerial vehicles (UAVs) further strengthen MEC by offering flexible deployment, mobility, and reliable line-of-sight communication, making them suitable for temporary high-demand scenarios. Moreover, such latency-sensitive applications often generate numerous repetitive tasks and, thus, storing the results of these tasks can reduce both communication overhead and computational workload. However, jointly addressing the caching of task-results alongside offloading and resource allocation decisions in UAV-aided MEC networks remains a non-trivial challenge. In this study, an integrated task offloading and resource allocation with data caching (JORC) framework is proposed to address these challenges. The offloading and resource allocation problems are formulated as a Markov decision process and solved using the soft actor–critic reinforcement learning algorithm. In addition, dynamic and adaptive caching manages limited storage and reduces redundant computations by using a hybrid strategy that integrates the least-frequently used and least-recently used policies to reduce computational redundancy. Simulation results confirm that the proposed JORC framework substantially reduces latency, energy consumption, and overall system cost, while increasing the successful task completion ratio compared to existing baseline approaches. Full article

Measuring the water table level is a critical factor in irrigated agriculture in arid regions, as it can significantly influence the exchange of water and nutrients with crops. This work presents the design, implementation, and field validation of an open-source, solar-powered IoT device for autonomous groundwater level monitoring, combining long-range low-power LoRa communication, a non-contact pressure-based level sensor using the trapped-air capillary method, and an efficient power management stage that seamlessly switches between solar and battery power. Unlike existing commercial leveloggers, which are costly and lack integrated wireless telemetry and solar-based autonomy, the proposed platform is presented as a fully open-source, low-cost alternative purpose-built for unattended deployment in areas without grid power or cellular coverage. The system was validated through a multi-day field trial and dedicated communication tests, demonstrating a stable power conversion efficiency of 84–90%, a five-day autonomous operation without any deep-discharge event, high linearity ($R^2$ = 0.9998) of the level module over a 0–2 m range with a resolution of approximately 1.94 mm per ADC count, and a reliable LoRa link of up to 8.51 km in an urban/suburban environment despite non-line-of-sight conditions. With an estimated hardware cost of approximately $100 USD per unit, the device represents a low-cost, low-maintenance tool capable of generating knowledge about water resources to optimize irrigation and crop management in the face of climate change. Full article

Aiming at the problem of high-precision positioning difficulties caused by the complete loss of lock of GNSS signals, strong multipath, and non-line-of-sight (NLOS) propagation in indoor pseudolite positioning, this paper builds a pseudolite positioning prototype system for small-scale indoor scenarios and proposes a Northern Goshawk Optimization-based ambiguity function method (AFM) single-epoch resolution algorithm (AFM-NGO). First, this method constructs the ambiguity function using double-difference carrier phase observations, takes the 3D coordinates of the station as the search variable, and jointly estimates the coordinates and integer ambiguities in the coordinate domain. Then, the Northern Goshawk swarm intelligence optimization algorithm is introduced to perform global and local collaborative search on the AFM search space, avoiding the large computational load of traditional grid search. Meanwhile, the statistical characteristics of multipath residuals and observation noise are explicitly considered in the fitness function, thereby enhancing the robustness of the algorithm in complex indoor environments. Based on a 6 m × 5 m × 2 m indoor strong multipath experimental scenario, 2D and 3D positioning tests were carried out on the pseudolite system. The results show that the proposed AFM-NGO algorithm can achieve stable centimeter-level positioning accuracy through single-frequency single-epoch carrier phase observations without initialization using high-precision known points; the error curve of consecutive epochs is smooth with no obvious outliers. Compared with traditional pseudolite positioning methods, it has smaller 3D root mean square error (RMSE) and better temporal stability of errors, which verifies the effectiveness and engineering application prospects of the algorithm in indoor strong multipath pseudolite positioning applications. Full article

The automotive industry is evolving toward Software-Defined Vehicles (SDVs) enabled by centralized computing, cloud integration, and Over-the-Air (OTA) updates. Yet, prevailing SDV and Internet of Vehicles (IoV) simulators often treat each vehicle as a single monolithic node, obscuring the interplay between internal vehicle modules and the surrounding infrastructure in dense urban scenarios. This work proposes a modular SDV reference architecture embedded in a Software-Defined Internet of Vehicles (SD-IoV) framework together with a Software-in-the-Loop (SiL) co-simulation testbed built on Objective Modular Network Testbed in C++ (OMNeT++), Simulation of Urban MObility (SUMO), and Vehicles in Network Simulation (Veins). The architecture decouples perception, communication, decision, and actuation into typed replaceable modules and instantiates them across six co-existing agent types: an SDV; two human-driver vehicle classes with cognition modelled as a multi-stage Eye–Ear–Brain–Hand–Foot pipeline with reaction-delay sampling; a public transport bus; a Roadside Unit (RSU); and a Traffic Light (TL). Three platform-level mechanisms connect the agents to the infrastructure: a single shared world model with a three-layer line-of-sight funnel that serves visual-sensor queries and reuses the building polygons of the wireless shadowing model; a dual-CPU mobile-fog node implementing a cycles-per-frequency workload model with explicit end-to-end latency decomposition; and a three-plane intersection coordination fabric that combines 802.11p wireless with a wired RSU-to-TL star and a wired peer mesh between adjacent TLs. The initial results confirm that the implemented message paths and module interactions behave as specified, including directional Signal Phase and Timing (SPaT) reception, cross-junction handover, bus-side fog-offload latency accounting, and passive identification of Vehicle-to-Everything (V2X)-silent vehicles. Several architecture elements are specified but deliberately not exercised in the present evaluation and remain design targets for future work: the Roadside Unit (RSU) route planning and fog computing companion (and any multi-tier offloading comparison), non-line-of-sight SPaT reception, and a safety violation detection layer. Within the above scope, the testbed is positioned as a reusable foundation for module-level SDV research and as a basis for future extensions such as Joint Communication and Sensing (JCAS), energy-aware driving, and Hardware-in-the-Loop (HiL) integration. Full article

Stratospheric airship navigation requires accurate wind field measurements at a ~20 km altitude, where low pressure and density limit the effectiveness of conventional wind sensors. To address this, we present a compact direct-detection Rayleigh Doppler wind lidar based on the molecular double-edge technique. The system utilizes a 532 nm fiber-coupled pulsed laser (0.5 W, 5 ns) and a fixed-cavity dual-channel Fabry–Perot etalon as the frequency discriminator. A liquid crystal variable retarder (LCVR) combined with a polarization beam splitter (PBS) enables non-mechanical, high-speed beam switching between two orthogonal line-of-sight (LOS) directions for horizontal wind measurement. Systematic tests are performed in controlled wind fields within Mie-dominated and Rayleigh-dominated regimes. The lidar effectively captures the sharp radial velocity profiles at wind speeds up to 7.6 m/s. Comparative experiments with a reference anemometer show that the system delivers reliable performance at 0.48 m range resolution, with measurement uncertainty below 0.34 m/s. With its compact, lightweight, and high-precision design, the developed lidar demonstrates reliable wind measurement capability under laboratory conditions, indicating its potential for future deployment on stratospheric airships. Full article

Robotic systems for search-and-rescue operations require robust, non-line-of-sight victim detection in order to locate trapped individuals behind obstacles with high precision. This paper presents a robotic victim-localization system based on a convolutional neural network—vision transformer (CNN-ViT) architecture trained on an open-source radar dataset for through-wall presence detection. In addition to binary presence detection, the proposed approach uses attention information from the transformer layers to estimate the region of the radar signal most relevant to the victim location. The model is deployed on a robotic platform and tested in an additional realistic environment, where classification and distance-estimation outputs are fused into a heatmap-style spatial representation. This enables the system to localize the estimated victim position on the map generated by the robot. To enhance robustness, the system is evaluated using both a leave-one-subject-out (LOSO) protocol on the original open-source radar dataset and additional experimental sessions collected with the robotic platform. On the original dataset, the model achieved victim-detection F1 scores of 82–96% and distance-estimation MAE of 0.25–0.65 m relative to the robot. On newly collected, previously unseen data, it achieved F1 scores of 72–92% and an MAE of 0.16–0.78 m on correctly classified present samples. The complete pipeline was further deployed on a mobile robot in an environment different from the one used to collect the original dataset, illustrating the potential of the proposed system for practical search-and-rescue scenarios. Full article

The transition to Beyond fifth generation of wireless networks (B5G) and sixth generation of wireless networks (6G) exposes the severe interference and coverage limitations of conventional cell-centric architectures. To overcome these bottlenecks, this paper presents a scalable four-layer radio network planning framework that jointly optimizes the deployment of distributed active antenna arrays and passive reconfigurable intelligent surfaces (RISs). The proposed framework integrates a digital twin (DT) loop within an Open-RAN (O-RAN) architecture, employing multi-agent deep reinforcement learning (MADRL) and fractional programming (FP) for real-time joint active and passive beamforming optimization. Extensive Monte Carlo simulations in a dense urban environment demonstrate a 45% increase in spectral efficiency, a 30% reduction in uplink interference, and an 84% reduction in coverage holes compared to legacy 5G networks. Ultimately, these results provide network operators with a cost-effective, standards-compliant blueprint to extend non-line-of-sight (NLOS) coverage by 40% without incurring the prohibitive capital expenditure (CAPEX) of dense active hardware deployments. Furthermore, the proposed architecture demonstrates a competitive 10–15% margin of improvement in spectral efficiency over recent state-of-the-art DRL-based RIS frameworks. Full article

This paper investigates an active reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) system with transmitter hardware impairments (HWIs) at the base station (BS). The active RIS provides both phase adjustment and amplitude amplification, which helps mitigate the multiplicative fading effect of passive RIS-assisted cascaded links and establish virtual line-of-sight (LoS) links for the target and multiuser communication users. Considering the coupling among the BS transmitter distortion noise, active RIS amplification noise, and the active RIS power constraint, we formulate a radar output signal-to-noise ratio (SNR) maximization problem. The radar output SNR is maximized by jointly designing the radar receive filter, the BS transmit beamforming matrix, and the active RIS reflection coefficients, while satisfying the quality-of-service (QoS) requirements of communication users, the BS transmit power constraint, and the active RIS power budget constraint. To solve the resulting non-convex problem with fractional objectives and high-order coupling terms, an alternating optimization (AO)-based iterative framework is developed. Specifically, the radar receive filter is updated using the generalized Rayleigh quotient, the BS transmit beamforming subproblem is handled by semidefinite relaxation and the Charnes–Cooper transformation, and the active RIS reflection coefficient subproblem is solved using the Dinkelbach transformation and majorization–minimization. Simulation results demonstrate stable convergence and show that the proposed design improves radar output SNR under BS transmitter HWIs. Full article

Ultra-wideband (UWB) and inertial measurement unit (IMU) fusion is an effective scheme for train and rail-guided target localization because UWB provides absolute ranging results and the IMU provides high-rate motion prediction. In practical rail transportation systems, UWB anchors are usually deployed along the track with large longitudinal intervals and limited lateral separation to reduce installation and maintenance costs. This anchor deployment leads to a large condition number of the observation matrix, so slight ranging errors caused by non-line-of-sight (NLoS) propagation may be amplified into large localization errors. To mitigate LoS/NLoS interference, this article proposes a propagation reliability estimation method for track-constrained UWB/IMU fusion localization. First, rail transportation localization along a narrow path is formulated as a one-dimensional track-constrained problem, and each UWB ranging result is converted into a candidate longitudinal coordinate on the known track centerline. Second, the reliability of each anchor–target propagation is estimated in a sliding window by comparing the motion increments solved by UWB observations with the motion prediction by the IMU. Third, the estimated reliability is incorporated into a reliability-weighted track-domain update before a closed-loop position–velocity Kalman correction. The simulation results show that, under the mixed LoS/NLoS scenario, the proposed method achieves an NLoS-interval RMSE of 0.0090 m. Compared with Track-EKF, Track-Gauss-AUKF, Track-Adaptive KF, and Track-SW-FGO, the proposed method reduces the NLoS-interval RMSE by 92.9%, 62.1%, 92.8%, and 92.6%. A supplementary real-data stress test on the public STAR-loc dataset demonstrates an average longitudinal RMSE of 0.0555 m under a strict online calibrated-range protocol, supporting the algorithm’s practical applicability against real-world lateral sway and asynchronous sensor noise. Full article

In open-pit mining environments, large equipment frequently causes severe occlusion, creating critical perception blind spots for LiDAR. Meanwhile, 4D millimeter-wave (mmWave) radar provides penetration capability but is highly susceptible to multipath interference generated by metallic structures and uneven terrain. To address these challenges, this paper proposes a Blind-Spot Complementary Fusion (BSCF) framework that integrates 3D LiDAR and 4D mmWave radar through explicit geometric constraints. The proposed framework first suppresses multipath artifacts and performs calibrated spatiotemporal alignment between heterogeneous sensors. It then introduces high-confidence radar observations into LiDAR blind spots through spatial consistency verification, providing existence-level hidden-target risk cues under extreme occlusion. In addition, a Volume Recovery Rate (VRR) proxy metric is proposed to quantitatively describe envelope-level spatial evidence in occluded regions. Experiments conducted on real-world mining datasets demonstrate that the proposed method effectively suppresses severe multipath interference and improves scene-level cross-modal proximity by approximately 16.5%, while the overlap-region RMSE remains approximately 0.18 m. Under complete occlusion where LiDAR observations are unavailable, the framework provides target-existence risk cues with a VRR of up to 15.6%, providing a useful preliminary indication of hidden target existence. Ultimately, the proposed approach enhances perception robustness and safety for autonomous transportation systems in open-pit mines. Full article