Search Results (366)

High-Frequency (HF) geolocation is crucial for emergency search and rescue operations and for re-geolocation of missing targets. This paper proposes a two-stage (Receiver selection then geolocation with Random Spatial Spectrum (RSS)) framework with HF skywave propagation as the main link, which is suitable for scenarios where the electric Vertical Take-off and Landing (eVTOL) aircraft loses contact, crashes, or has communication interruption after a malfunction. First, stage A employs two receiver selection paths. One is selection with unknown biases, which combines geometric observability to determine receiver selection. The other is selection with bias priors, which introduces non-line-of-sight bias priors and robust weighting to improve availability. Secondly, stage B constructs RSS-based geolocation using grid objective function matching to alleviate the sensitivity of explicit time difference estimation to noise and model mismatch, thereby maintaining robustness under non-line-of-sight (NLOS) conditions. Finally, simulation and actual measurements demonstrate that the “select first, geolocation later” approach achieves superior overall performance compared to direct geolocation without receiver selection. This study provides a methodological basis and initial field evidence for HF skywave-based emergency eVTOL geolocation. Full article

We present an experimental demonstration of a daylight-capable Optical Amplify-and-Forward (OAF) relaying system designed to support flexible and high-capacity network topologies. The proposed architecture integrates fiber-based infrastructure with OAF Free Space Optics (FSO) relaying, enabling bidirectional optical communication over 460 m (x2) using SFP-compatible schemes, while addressing Non-Line-of-Sight (NLOS) constraints and fiber disruptions. This work achieves a Bit Error Rate (BER) below the Hard-Decision Forward Error Correction (HD-FEC) limit, validating the feasibility of high-speed urban FSO links. By leveraging low-cost fiber-coupled optical terminals, the system transmits single-carrier 120 Gbps Intensity Modulation/Direct Detection (IM/DD) signals using NRZ (Non-Return-to-Zero) and PAM4 (4-Pulse Amplitude Modulation) modulation formats. Operating entirely in the optical C-Band domain, this approach ensures compatibility with existing infrastructure, supporting scalable mesh FSO deployments and seamless integration with hybrid Radio Frequency (RF)/FSO systems. Full article

Positioning technology based on 5G networks has been deeply integrated into everyday life. Despite this, severe non-line-of-sight (NLOS) conditions in wireless signal environments can cause signal obstructions, negatively impacting the precision and dependability of positioning services. This paper introduces an innovative algorithm called Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface Non-Line-of-Sight Angle of Arrival (STAR-RIS NLOS AOA) to address these challenges. The algorithm initially develops a system model named 5G STAR-RIS localization (GSL). By integrating STAR-RIS into the system, the model effectively overcomes the challenges of positioning in NLOS scenarios. The inclusion of STAR-RIS not only boosts the system’s adaptability but also meets the positioning requirements for users on both sides of the reflective surface simultaneously. The algorithm then utilizes the Root-MUSIC algorithm for estimating user coordinates. An optimization problem is formulated based on these estimations, with the goal of reducing the gap between estimated and real coordinates. To address this optimization, the Inertia Weight Whale Optimization Algorithm is employed, providing high-precision estimations of users’ three-dimensional positions. Simulations reveal that the proposed Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface Non-Line-of-Sight Angle of Arrival (SRNA) algorithm substantially outperforms conventional algorithms in positioning performance across different signal-to-noise ratio contexts. Specifically, in challenging NLOS situations, the SRNA algorithm can cut positioning errors by 50% to 62%, demonstrating its outstanding capability and efficiency in addressing the difficulties presented by NLOS conditions within 5G-based positioning systems. Full article

In Ultra-Wide Band (UWB) positioning, wireless signals are subject to non-line-of-sight (NLOS) propagation due to obstruction by obstacles, which leads to ranging and positioning estimation errors. How to accurately and efficiently identify line-of-sight (LOS) and NLOS propagation paths is a key research task in UWB positioning systems. By effectively integrating the characteristics of global channel impulse response (CIR) sequence features and statistical time-domain features, a dual-branch feature fusion Transformer (DBFF-Transformer) is proposed for NLOS path identification. Firstly, the original CIR sequence data is processed using the Transformer to learn the global feature relationships within the data. Secondly, four key time-domain features are extracted from the CIR sequence: the first-path energy ratio, the root-mean-square time delay spread, the kurtosis and the phase difference. Finally, by integrating the sequence features and the time-domain features, the two features’ branches are fused through a fully connected network. The proposed method is evaluated in two typical indoor scenarios from the latest open-source datasets of the eWINE project. The ablation experiment proves that the fusion of the sequence features and time-domain features of the CIR sequence can effectively improve NLOS identification accuracy. The identification accuracy in the two experimental scenarios is 95.9% and 95.7%, with F1 scores of 97.2% and 97.1% and Recall of 97.4% and 96.4%, respectively. The comparative analysis of the DBFF-Transformer with the state-of-the-art baseline models demonstrates superior accuracy and robustness, which can provide a novel solution for NLOS identification in UWB indoor positioning. Full article

Long-Range Frequency Hopping Spread Spectrum (LR-FHSS) is the newest modulation in LoRaWAN, designed to overcome the scalability and coverage limits of conventional LoRa. This study provides a real-world evaluation of LR-FHSS performance, benchmarking it directly against LoRa. An outdoor campaign was conducted in urban and semi-urban scenarios in and near the city of Castelldefels using a complete LR-FHSS-enabled network and an end-device transmitting at LoRa and LR-FHSS data rates (DRs). Measurements were collected along four diverse paths, capturing key metrics such as Received Signal Strength Indicator (RSSI) and Packet Delivery Ratio (PDR). The results clearly underline the advantages of LR-FHSS; while LoRa at DR0 and DR5 quickly lost connectivity beyond 1.5–2 km, LR-FHSS, particularly at DR8 and DR10, kept reliable links at 3–4 km. LR-FHSS robustness was most evident in non-line-of-sight (NLoS) and long-range scenarios. These findings highlight LR-FHSS as a strong candidate for future IoT deployments, offering extended range and higher robustness in challenging environments. Full article

Among wireless communication technologies underlying Internet of Things (IoT)-based smart buildings, IQRF (Intelligent Connectivity Using Radio Frequency) technology is a promising candidate due to its low power consumption, cost-effectiveness, and wide coverage. However, effectively modeling the propagation characteristics of IQRF in complex indoor environments for simple and accurate network deployment remains challenging, as architectural elements like walls and corners cause substantial signal attenuation and unpredictable propagation behavior. This study investigates the applicability of a site-specific modeling approach, originally developed for urban street canyons, to characterize peer-to-peer (P2P) IQRF links operating at 868 MHz in typical indoor scenarios, including line-of-sight (LoS), one-turn, and two-turn non-line-of-sight (NLoS) configurations. The received signal powers are compared with well-known empirical models, including international telecommunication union radio communication sector (ITU-R) P.1238-9 and WINNER II, and ray-tracing simulations. The results show that while ITU-R P.1238-9 achieves lower prediction error under LoS conditions with a root mean square error (RMSE) of 5.694 dB, the site-specific approach achieves substantially higher accuracy in NLoS scenarios, maintaining RMSE values below 3.9 dB for one- and two-turn links. Furthermore, ray-tracing simulations exhibited notably larger deviations, with RMSE values ranging from 7.522 dB to 16.267 dB and lower correlation with measurements. These results demonstrate the potential of site-specific modeling to provide practical, computationally efficient, and accurate insights for IQRF network deployment planning in smart building environments. Full article

Enhancing the positioning stability and accuracy of autonomous following systems poses a significant challenge, particularly in dynamic indoor environments susceptible to occlusion and interference. This paper proposes an innovative approach that integrates Ultra-Wideband (UWB) technology with computer vision-based gait analysis to overcome these limitations. First, a low-power, high-update-rate UWB positioning network is established based on an optimized Double-Sided Two-Way Ranging (DS-TWR) protocol. To compensate for UWB’s deficiencies under Non-Line-of-Sight (NLOS) conditions, a visual gait recognition process utilizing the GaitPart framework is introduced for target identification and relative motion estimation. Subsequently, an Extended Kalman Filter (EKF) is developed to seamlessly fuse absolute UWB measurements with gait-based relative kinematic information, thereby generating precise and robust estimates of the leader’s trajectory. This estimated path is tracked by a differentially driven mobile platform via a Model Predictive Controller (MPC). Experimental results demonstrate that the tracking deviation for most trajectory points remains within 50 mm, with a maximum observed deviation of 115 mm during turns, confirming its strong robustness and practical utility in real-world intelligent vehicle applications. Full article

Existing Ultra-Wideband (UWB) positioning methods are poorly suited for underground mobile devices and have limited positioning effectiveness in complex scenarios such as narrow tunnels, high dust levels, metallic structures, moving personnel, and machinery. To address this, we propose a UWB positioning method for non-line-of-sight (NLOS) error compensation, significantly improving the positioning accuracy of mobile equipment in coal mine tunnels. First, the characteristics of the impulse response waveform channel of the dataset are extracted, and the AdaBoost-based ensemble learning method is used to identify the mixture propagation channel. Then, combined with the UWB range noise model, the extended Kalman filter (EKF) algorithm is used to compensate for UWB NLOS errors. Finally, a mobile tag is used in conjunction with four positioning base stations to obtain positioning data, and the positioning effect in coal mine tunnels is simulated using a ranging noise model. The experimental results show that the EKF error compensation algorithm has good positioning accuracy and algorithm stability in different motion states in a noisy environment. Full article

To address the frequent safety incidents caused by positioning uncertainty due to NLOS (Non-Line-of-Sight) interference in complex manufacturing workshop environments, this paper aims to achieve high-precision distance measurement and positioning in complex workshop scenarios. First, common NLOS identification methods are analyzed. By combining received signal energy and ranging residuals, a rapid NLOS identification method is proposed. Building on this foundation, a ranging error compensation method based on maximum likelihood estimation and adaptive extended Kalman filtering is designed. Finally, static experiments are conducted to verify the effectiveness of the proposed NLOS identification method and ranging error compensation approach. Experimental results indicate that the ranging accuracy of the proposed method has been significantly improved and demonstrates considerable advantages over traditional Kalman filtering algorithms. Full article

With the rapid proliferation of unmanned aerial vehicles (UAVs) and the associated rise in security concerns, there is a growing demand for robust detection and identification systems capable of operating reliably in complex electromagnetic environments. To address this challenge, this paper proposes a deep learning-based passive UAV detection and identification system leveraging radio frequency (RF) spectrograms. The system employs a high-resolution RF front-end comprising a multi-beam directional antenna and a wideband spectrum analyzer to scan the target airspace and capture UAV signals with enhanced spatial and spectral granularity. A YOLO-based detection module is then used to extract frequency hopping signal (FHS) regions from the spectrogram, which are subsequently classified by a convolutional neural network (CNN) to identify specific UAV models. Extensive measurements are carried out in both line-of-sight (LoS) and non-line-of-sight (NLoS) urban environments. The proposed system achieves over 96% accuracy in both detection and identification under LoS conditions. In NLoS conditions, it improves the identification accuracy by more than 15% compared with conventional full-spectrum CNN-based methods. These results validate the system’s robustness, real-time responsiveness, and strong practical applicability. Full article

To address the problem of shallow-sea hydrophone calibration, this paper proposes a shallow-sea hydrophone calibration algorithm for the horizontal and depth directions, respectively. In the horizontal direction, a calibration method combining an improved Particle Swarm Optimization (PSO) algorithm and the Time Difference Of Arrival (TDOA) algorithm is proposed. In the depth direction, a depth calibration formula using the time delay difference between Non-Line-of-Sight (NLOS) waves and Line-of-Sight (LOS) waves is put forward. By combining this with the proposed PSO algorithm, the PSO NLOS–LOS depth correction algorithm is obtained. The specific position of the hydrophone is determined by combining the algorithms for horizontal direction and depth. The advantages of the proposed algorithms are verified through simulations and experiments. Simulations show that in the horizontal direction, the proposed algorithm can reduce the average calibration error under different hydrophone array radii to 0.8690 m. In the depth direction, the specific propagation delay is unknown. Compared with the traditional depth calculation method, which requires the specific propagation delay to be known, the algorithm proposed in this paper can reduce the impact on depth calculation caused by delay deviation due to sound ray refraction; in addition, it provides stronger robustness and more accurate depth calibration in shallow sea environments. The new method shows significant improvement in the depth calculation process compared with the traditional algorithm, especially in terms of fault tolerance for errors in the horizontal direction. Experiments show that by combining the calibration algorithms proposed in this paper, the positioning accuracy of the hydrophone array is significantly improved and the average positioning error of the hydrophone array is reduced to within 12 m. Full article

To mitigate the degradation of positioning accuracy in sixth-generation mobile communication systems under dynamic line-of-sight (LOS) and non-line-of-sight (NLOS) conditions, this paper proposes an improved adaptive Kalman filter positioning method based on Orthogonal Time Frequency Space (OTFS)-modulated signals. Firstly, the distance can be measured by using the OTFS-modulated signals transmitted between base stations and nodes. Secondly, the distance information is converted into the distance difference information to establish the time difference of arrival (TDOA) positioning equation, which is preliminarily solved using the Chan algorithm. Thirdly, residuals are calculated based on the preliminary positioning results, dividing the complex environment into distinct regions and adaptively determining corresponding genetic factors for each region. Finally, the selected genetic parameters are substituted into the Sage–Husa adaptive Kalman filter equations to estimate positioning results. The simulation analysis demonstrates that in complex environments featuring both line-of-sight and non-line-of-sight conditions, the vehicle motion trajectories estimated using this method more closely approximate actual trajectories. Additionally, both the accuracy and stability of positioning results show significant improvement compared to traditional methods. Full article

This work presents a closed-form approximation to the symbol error probability (SEP) for cross-quadrature amplitude modulation (cross-QAM) schemes over $\mathit{\kappa }$–$\mathit{\mu }$ fading channels. The proposed formulation enables accurate performance evaluation while avoiding computationally expensive numerical integration. The analysis covers millimeter-wave (mmWave) frequencies at 55, 60, and 65 GHz, under both line-of-sight (LoS) and non-line-of-sight (nLoS) conditions, and for multiple transmitter–receiver polarization configurations. A key contribution of this work is the experimental validation of the theoretical expression with real channel-measurement data, which confirms the applicability of the $\mathit{\kappa }$–$\mathit{\mu }$ model in realistic mmWave scenarios. Furthermore, we perform a detailed parametric study to quantify the influence of $\mathit{\kappa }$ and $\mathit{\mu }$ on adaptive modulation performance, providing practical insights for 5G and future 6G systems. The proposed framework bridges theoretical analysis and experimental validation, offering a computationally efficient and robust tool for the design and evaluation of advanced modulation schemes in generalized fading environments. Full article

This paper studies the problem of locating wireless sensor networks (WSNs) based on time-of-arrival (TOA) measurements in mixed line of sight/non-line-of-sight (LOS/NLOS) environments. To mitigate the impacts of NLOS and improve performance both in positioning accuracy and computation time, we hereby propose an exhaustive method (i.e., EM). The EM method mainly consists of two processes. In the first process, all BSs are arranged into various combinations. For each combination, a solution and its corresponding residual vector can be obtained. For each combination, all BSs can be divided into two categories: BSs that participate in positioning and BSs that do not. Therefore, the above residual vector can also be divided into two categories in each group. In the second process, combining the comparison results of two residual vectors and the characteristics of NLOS errors, we propose a new criterion to find out solutions with only LOS-BSs. Then the final solution can be obtained by further processing these solutions. This method does not require any prior information regarding NLOS status, NLOS amplitude, or noise variance, and only needs three LOS-BSs. Numerical simulation results shows that our method greatly improves the accuracy and reduces the computation time compared to state-of-art methods. Full article

Indoor localization of fast-moving targets under asynchronous acoustic sensing is severely constrained by non-line-of-sight (NLOS) propagation and sparse anchor deployments. To overcome these limitations, we propose a trajectory reconstruction-based framework that simultaneously exploits time-of-arrival (ToA) and frequency-of-arrival (FoA) measurements. By embedding temporal continuity and motion dynamics into the localization model, we cast the problem as a constrained nonlinear least squares optimization over the entire trajectory rather than isolated snapshots. To efficiently solve this high-dimensional problem, we design an enhanced particle swarm optimization (PSO) algorithm featuring adaptive phase switching and noise-resilient updates. Simulation results under varying noise conditions show that our method achieves superior accuracy and robustness compared to conventional least squares estimators, especially for high-speed trajectories. Real-world experiments using a passive acoustic testbed further validate the effectiveness of the proposed framework, with over 90% of localization errors confined within 3 m. The method is model-driven, training-free, and scalable to asynchronous and anchor-sparse environments. Full article