Search Results (219)

Aiming at the shortcomings of traditional ACO algorithms in indoor localization applications, a high-performance improved ant colony algorithm (HIPACO) based on dynamic hybrid pheromone strategy is proposed. The algorithm divides the ant colony into worker ants (local exploitation) and soldier ants (global exploration) through a division of labor mechanism, in which the worker ants use a pheromone-weighted learning strategy for refined search, and the soldier ants perform Gaussian perturbation-guided global exploration. At the same time, an adaptive pheromone attenuation model (elite particle enhancement, ordinary particle attenuation) and a dimensional balance strategy (sinusoidal modulation function) are designed to dynamically optimize the searching process; moreover, a hybrid guidance mechanism is introduced to apply adaptive Gaussian perturbation guidance on successive failed particles to dynamically optimize the searching process. A hybrid guidance mechanism is introduced to enhance the robustness of the algorithm by applying adaptive Gaussian perturbation to successive failed particles. The experimental results show that in the 3D localization scenario with four beacon nodes, the average localization error of HIPACO is 0.82 ± 0.35 m, which is 42.3% lower than that of the traditional ACO algorithm, the convergence speed is improved by 2.1 times, and the optimal performance is maintained under different numbers of anchor nodes and spatial scales. This study provides an efficient solution to the indoor localization problem in the presence of multipath effect and non-line-of-sight propagation. 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

This paper presents an angles-only navigation (AON) method utilizing optical observations of a single satellite with known ephemeris and prior altitude constraints given by an altimeter or known topography, which can enable near-ground platforms to achieve autonomous navigation in GNSS-denied environments. By leveraging a star tracker to measure the line-of-sight (LOS) direction of a satellite against a star background, the observer’s location is resolved via triangulation under geometric constraints. Theoretical error models are derived to analyze the influence of satellite position errors, LOS direction errors, and altitude uncertainties on geolocation accuracy. Numerical simulations validate the error propagation mechanisms, demonstrating that geolocation error is primarily determined by the perpendicular projection of orbital error relative to the LOS, increases linearly with LOS distance, and is sensitive to altitude errors at low elevation angles. Ground-based experiments conducted using Globalstar satellites achieve geolocation accuracy within 250 m (RMS), consistent with theoretical predictions. The proposed method offers a practical, low-cost solution for high-precision passive navigation in maritime and terrestrial applications. 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

Accurate estimation of path loss is essential for evaluating the impact of the propagation medium, determining transmission power requirements, and optimizing cell layouts for effective 5G millimetre wave coverage. At 28 GHz, rain attenuation is a critical factor, with its impact varying significantly based on environmental and regional characteristics. This study quantifies the degradation of 5G millimetre wave link budgets due to rainfall in South Africa and assesses the maximum coverage ranges for urban micro and urban macro deployments under varying rain intensities. The analysis focuses on Pretoria, a city characterized by diverse urban landscapes and seasonal thunderstorms. Urban micro cells are deployed on streetlights and building facades in dense zones such as Hatfield and Sunnyside to deliver high-capacity coverage. In contrast, urban macro cells target broader coverage from elevated structures, such as those in the Pretoria CBD. Using the Close-In path loss model for both line-of-sight and non-line-of-sight conditions, this study examines the relationships between link budget parameters, maximum path loss, and 5G millimetre wave link distances under rain-affected and clear-sky scenarios. The results highlight the significant influence of rainfall, particularly in non-line-of-sight conditions, and provide insights for designing efficient 5G networks tailored to South Africa’s unique climate. Full article

The traditional radio wave propagation models exhibit several limitations when they are employed to predict the path loss for vehicular ultra-shortwave wireless communication. To addresses these challenges, an optimized approach for Egli model based on the high-resolution remote sensing satellite image is proposed in this study. The optimization process includes three components. First, a method for calculating the actual equivalent antenna height is introduced, utilizing high-precision remote sensing satellite imagery to obtain communication path profiles. This method accounts for the antenna’s physical length, vehicular height, and local terrain characteristics, thereby providing an accurate representation of the antenna’s effective height within its operational environment. Second, an equivalent substitution method for ground loss is developed, utilizing surface information derived from high-precision remote sensing satellite images. This method integrates ground loss directly into the Egli model’s calculation process, eliminating the need for separate computations and simplifying the model. Third, leveraging the Egli model as a foundation, the least squares method (LSM) is employed to fit the relief height, ensuring the model meets the requirements for ultra-short wave communication distances under line-of-sight (LOS) conditions and enhances suitability for real-world vehicular communication systems. Finally, the validity and accuracy of the optimization model are verified by comparing the measured data with the theoretical calculated values. Compared with the Egli model, the Egli model with additional correction factors, and the measured data, the average error of the optimized model is reduced by 8.98%, 2.09%, and the average error is 0.45%. Full article

A novel partial discharge (PD) source localization method was proposed based on the traditional time difference in arrival (TDOA) method. Specifically, the non-line-of-sight (NLOS) propagation phenomenon of the ultra-high-frequency (UHF) signal was considered, and the NLOS propagation error was approximately replaced by a constant, thereby limiting the effect of NLOS propagation. Moreover, the strategy of utilizing more than four sensors was adopted to reduce the possible effect of overcorrection on NLOS propagation. In this paper, the derivation and implementation process of the proposed method is introduced from the perspectives of mathematical model and geometrical model, and its localization results were compared with the traditional TDOA method through an experimental study. The results showed that the speed of error increase of the traditional method presented faster, and the increment of sensor number helped to improve the localization accuracy, but the reduction in localization error becomes insignificant when the sensors exceed six. Finally, the experimental verifications were conducted based on a 35 kV testing transformer with six sensor installations. The experiments found that the proposed localization method had a better calculated accuracy and stability; the obtained minimum calculated error was 10.88 cm, the calculated accuracy can be improved by 82.04% and 78.94%, respectively, with six sensors than four and five sensors arrangement. 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

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

The accurate and reliable positioning of unmanned aerial vehicles (UAVs) in urban environments is crucial for urban air mobility (UAM) application, such as logistics, surveillance, and disaster management. However, global navigation satellite systems (GNSSs) often fail in densely populated areas due to signal reflections (multipath propagation) and obstructions non-line-of-sight (NLOS), causing significant positioning errors. To address this, we propose a machine learning (ML) framework that integrates 5G position reference signals (PRSs) to correct UAV position estimates. A dataset was generated using MATLAB’s UAV simulation environment, including estimated coordinates derived from 5G time of arrival (TOA) measurements and corresponding actual positions (ground truth). This dataset was used to train a fully connected feedforward neural network (FNN), which improves the positioning accuracy by learning patterns between predicted and actual coordinates. The model achieved significant accuracy improvements, with a mean absolute error (MAE) of 1.3 m in line-of-sight (LOS) conditions and 1.7 m in NLOS conditions, and a root mean squared error (RMSE) of approximately 2.3 m. The proposed framework enables real-time correction capabilities for dynamic UAV tracking systems, highlighting the potential of combining 5G positioning data with deep learning to enhance UAV navigation in urban settings. This study addresses the limitations of traditional GNSS-based methods in dense urban environments and offers a robust solution for future UAV advancements. Full article

The advent of digital mining has become a tangible reality in recent years. This digital evolution requires a predictive understanding of key elements, particularly considering the reliable communication infrastructures needed for autonomous machines. The LoRa technology and its underground propagation behavior can make an important contribution to this digitalization. Since LoRa operates with a high signal budget and long ranges in sub-GHz frequencies, its behavior is very promising for underground sensor networks. The aim of the development and series of measurements was to observe LoRa’s applicability and propagation behavior in active salt mines and to detect and identify effects arising from the special environment. The propagation of LoRa was measured via packet loss and signal strength in line-of-sight and non-line-of-sight configurations over entire mining sections. The aim was to analyze the performance of LoRa at the macroscopic level. LoRa operated at 868 MHz in the free band, and units were equipped with omni-directional antennas. The K+S Group’s active salt and potash mine Werra, Germany, was kindly opened as a distinctive experimental setting. The LoRa exhibited characteristics that were highly distinctive in this environment. The presence of the massive salt allowed the signal to bounce along drift edges with near-perfect reflection, which enabled travel over kilometers due to a waveguide-like effect. A packet loss of below $15\%$ showed that LoRa communication was possible over distances exceeding 1000 m with no line-of-sight in room-and-pillar structures. Measured differences of $\Delta 50\mathrm{dBm}$ values confirmed consistent path loss across different materials and tunnel geometries. This effect occurs due to the physical structure of the mining drifts, facilitating the containment and direction of signals, minimizing losses during propagation. Further modeling and measurements are of great interest, as they indicate that LoRa can achieve even better outcomes underground than in urban or indoor environments, as this waveguide effect has been consistently observed. Full article

The starfish optimization algorithm (SFOA) is a metaheuristic evolutionary intelligence algorithm with a great global search capability and strong adaptability. Although the SFOA has a good global search capability, it is not accurate enough in local search and converges slowly. To further enhance this convergence ability and global optimization ability, an enhanced starfish optimization algorithm (SFOAL) is proposed that combines sine chaotic mapping, t-distribution mutation, and logarithmic spiral reverse learning. The SFOAL can remarkably enhance both the global and local convergence capabilities of the algorithm, leading to a more rapid convergence speed and greater stability. In total, 23 benchmark functions and CEC2021 were used to test the development, search, and convergence capabilities of the SFOAL. The SFOAL was compared in detail with other algorithms. The experimental results demonstrated that the overall performance of the SFOAL was better than that of other algorithms, and the joint strategy could effectively balance the development and search capabilities to obtain stronger global and local optimization capabilities. For solving practical problems, the SFOAL was used to optimize the back propagation (BP) neural network to solve the ultra-wideband line-of-sight positioning problem. The results showed that the SFOAL-BP neural network had a smaller average position error compared to the random BP neural network and the SFOA-BP neural network, so it can be used to solve practical application problems. Full article

Maritime relay communication has emerged as a critical application scenario for non-terrestrial networks (NTNs), providing beyond-line-of-sight (BLOS) connectivity for offshore terminals. Unlike terrestrial environments, the complex marine propagation conditions lead to signal instability. To enhance the robustness of maritime two-way relay networks (TWRNs), we propose a novel physical-layer network coding (PNC) scheme based on block Markov superposition transmission (BMST). The proposed scheme introduces a novel co-design framework that achieves dual breakthroughs: (1) robust error correction via BMST’s spatially coupled coding architecture and (2) spectral efficiency maximization through PNC’s spatial-domain signal superposition. Moreover, we develop a decoding–computing (DC) algorithm that sequentially performs iterative decoding followed by computing. Compared to the computing–decoding (CD) algorithm, the proposed DC algorithm mitigates useful information loss at relay nodes, achieving a 2.9 dB coding gain at a bit error rate (BER) of ${10}^{-5}$. Owing to the DC algorithm’s dual-layer decoding architecture, we can further improve the overall system performance through targeted optimization of either the code rate or memory size for communication sides with poor channel conditions, yielding an extra 0.2 dB gain at a BER of ${10}^{-5}$ compared to non-optimized configurations. The simulation results demonstrate that the proposed scheme significantly enhances maritime relay communication performance under harsh oceanic channel conditions while providing actionable insights for optimizing next-generation maritime communication system designs. Full article

Accurate indoor localization in wireless sensor networks remains a non-trivial challenge, particularly in complex environments characterized by signal variability and multipath propagation. This study presents a ZigBee-based localization approach that integrates multi-stage preprocessing of received signal strength indicator (RSSI) data with a reliability-aware extension of the maximum likelihood estimation (MLE) algorithm. To improve measurement stability, a hybrid filtering framework combining Kalman filtering, Dixon’s Q test, Gaussian smoothing, and mean averaging is applied to reduce the influence of noise and outliers. Building on the filtered data, the proposed method introduces a noise and link quality indicator (LQI)-based dynamic weighting mechanism that adjusts the contribution of each distance estimate during localization. The approach was evaluated under simulated and semi-physical non-line-of-sight (NLOS) indoor conditions designed to reflect practical deployment scenarios. While based on a limited set of representative test points, the method yielded improved positioning consistency and achieved an average accuracy gain of 11.7% over conventional MLE in the tested environments. These results suggest that the proposed method may offer a feasible solution for resource-constrained localization applications requiring robustness to signal degradation. Full article