Non-line-of-sight Research Articles

Indoor Human Action Recognition Based on Dual Kinect V2 and Improved Ensemble Learning Method.

Indoor human action recognition, essential across various applications, faces significant challenges such as orientation constraints and identification limitations, particularly in systems reliant on non-contact devices. Self-occlusions and non-line of sight (NLOS) situations are important representatives among them. To address these challenges, this paper presents a novel system utilizing dual Kinect V2, enhanced by an advanced Transmission Control Protocol (TCP) and sophisticated ensemble learning techniques, tailor-made to handle self-occlusions and NLOS situations. Our main works are as follows: (1) a data-adaptive adjustment mechanism, anchored on localization outcomes, to mitigate self-occlusion in dynamic orientations; (2) the adoption of sophisticated ensemble learning techniques, including a Chirp acoustic signal identification method, based on an optimized fuzzy c-means-AdaBoost algorithm, for improving positioning accuracy in NLOS contexts; and (3) an amalgamation of the Random Forest model and bat algorithm, providing innovative action identification strategies for intricate scenarios. We conduct extensive experiments, and our results show that the proposed system augments human action recognition precision by a substantial 30.25%, surpassing the benchmarks set by current state-of-the-art works.

On the capacity of a SIM-based cooperative NLOS UVC system with best relay selection

Ultraviolet communication (UVC) is a promising technology due to its ability to operate in non-line-of-sight (NLOS) mode thereby eliminating the pointing acquisition and tracking (PAT) requirement as needed by infrared and visible light communications. However, NLOS UVC suffers from very high attenuation and turbulence-induced fading when operated over a long distance. Due to these limitations, the existing literature on the NLOS UVC is mostly restricted to short-distance communications only. Therefore, this paper addresses these challenges by proposing an outdoor subcarrier-intensity-modulation (SIM) based multi-relay cooperative communication system employing the best relay selection and decode-and-forward (DF) relaying protocol. The turbulence-induced fading is modelled using lognormal distribution under weak atmospheric turbulence conditions. We derive novel closed-form analytical expressions of outage probability and ergodic capacity. Correctness of the derived analytical expressions is validated through numerical simulations.

Non-Line-of-Sight Full-Duplex Ultraviolet Communications Under Self-Interference

The full-duplex optical communications can be achieved simply by separating the transmitting link and the receiving link in the space. However, it is challenging to achieve the non-line-of-sight (NLOS) full-duplex ultraviolet (UV) communication due to the serious self-interference caused by the strong multiple scattering effects of UV signals. To explore the capacity of NLOS full-duplex UV communications, in this paper, we first quantify the self-interference using an analytical channel impulse response function. Based on the quantified self-interference, we then derive the error rate and the corresponding achievable information rate (AIR) for on-off keying modulation, 4-digital-pulse-interval modulation and 4-pulse-position modulation. We further propose a self-interference cancelling (SIC) method to mitigate the impacts of self-interferences. Simulation results show that the proposed SIC method can significantly improve the error rate and AIR performances. Besides, we find that the NLOS full-duplex UV communication will gradually lose its advantage over the NLOS half-duplex UV communication as either the communication distance or the elevation angle increases. However, using the proposed SIC method, the NLOS full-duplex UV communication can hold its advantage in a wide range of system geometries.

Design and Research of AGV Indoor Positioning System Based on Visual and Ultra Wideband Combination Positioning

Aiming at the problems of cumulative error in monocular visual positioning and Non-Line-of-Sight (NLOS) error in UWB positioning for the automated guided vehicle (AGV) in indoor environments, a combined method of vision and Ultra-Wide Band (UWB) is proposed for indoor AGV positioning. Firstly, the overall structure and system of the AGV are designed to achieve indoor navigation and positioning functions. Secondly, the monocular visual and UWB positioning data are fused using the Error State-Extended Kalman Filter algorithm (ES-EKF) to obtain the optimal pose estimation of the AGV. Finally, the AGV is used as a mobile platform to conduct positioning experiments in different indoor environments. The experimental results demonstrate that the navigation and positioning system has high accuracy and robustness in indoor environments with obstacles, and no significant drift or discontinuity phenomena occur during the positioning process, indicating its practicality in indoor settings.

Outage Analysis of UAV-Aided Networks With Underlaid Ambient Backscatter Communications

Ambient backscatter communication is an energy efficient technique for massive Internet of Things (IoT). Combining with flexibly deployed unmanned aerial vehicles (UAVs), the UAV-aided ambient backscatter communication can establish wireless links for isolated IoT nodes efficiently. In this paper, we investigate the outage performance of the UAV-aided air-ground network with underlaid ambient backscatter communications, where the emitted signals from the air-ground link are leveraged as radio frequency (RF) carrier for ambient backscattering. The air-ground channel is modeled as a probabilistic line-of-sight (LoS) channel with Nakagami- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> fading. Then, the ground communication is modeled as a non-line-of-sight (NLoS) channel with Rayleigh fading. For the downlink, we derive the expressions of the outage probabilities of the backscatter link and the air-ground link. In addition, the asymptotic cases of infinite transmit power and infinite fading parameter are analyzed. For the uplink, the outage probabilities of the backscatter link and air-ground are analyzed, with the cases of infinite transmit power and fading parameter discussed. Simulation results show that the analytical results match well with the Monte Carlo results, which verifies the effectiveness of the proposed scheme.

Coverage Performance Analysis of a Cache-Enabled UAV Base Station Assisted Cellular Network

Unmanned Aerial Vehicle base stations (UBSs) can be used to assist a ground cellular network to enhance its network services for cellular users. This paper studies the coverage performance analysis of a cache-enabled UBS-assisted cellular network. Analytical models are derived for investigating the overall coverage probability of the network and the average achievable rate of a cellular user. In deriving the analytical models, an air-to-ground (A2G) channel model with both a line-of-sight (LoS) link and a non-line-of-sight (NLoS) link, a BS association strategy based on the strongest average received power, and a cache model with a probabilistic caching strategy are considered. The cache hit probability of UBSs based on the cache model is also taken into consideration. Moreover, the association probabilities with the association strategy are derived for different types of base stations. The derived analytical models are validated through simulation results and the impacts of system parameters on the coverage performance of the network are investigated through numerical results. Compared with existing relevant work, the novelty of this work is that the effects of both an access link and a backhaul link are taken into account in the coverage performance analysis. The obtained results can provide theoretical guidance for the deployment of UBSs in a cache-enabled UBS-assisted cellular network.

A Novel Loosely Coupling Fusion Approach of Ultra-Wideband and Wheel Odometry for Indoor Localisation

Ultra-wideband (UWB) systems promise centimetre-level accuracy for indoor positioning, yet they remain susceptible to non-line-of-sight (NLOS) errors due to complex indoor environments. A fusion mechanism that integrates the UWB with an odometer sensor is introduced to address this challenge and achieve a high positioning accuracy. A sliding window method is applied to identify NLOS anchors effectively. The modified UWB-only positioning has an average error under 13 cm with an RMSE of 16 cm. Then, a loosely coupled approach named Dynamic Dimension Fusion (DDF) is designed to mitigate the odometer’s cumulative errors that achieve a remarkable average error and RMSE below 5 cm, notably superior to established unscented Kalman filter (UKF) fusion techniques. DDF utilises UWB data to correct the one-dimensional heading error of the odometer when the robot moves in a straight line and to correct both heading and mileage in two dimensions when the robot is turning. Comprehensive real-world experimental evaluations underscore the efficacy and robustness of this novel approach.

A Hybrid Differential Detection Scheme for the Ultra-Wideband Orientational Beamforming System

Differential space-time coding methods have been investigated for ultra-wideband communication systems to avoid channel estimation. However, their performance in the line-of-sight (LOS) environment is worse than the recently proposed orientational beamforming (OBF) system under low signal-to-noise ratios (SNRs). On the other hand, the OBF system cannot work well in the non-LOS (NLOS) environment. To address these issues, a hybrid differential detection (HDD) scheme is proposed in this paper, which combines the OBF system with a proposed differential OBF (DOBF) system. First, the DOBF system with a fully differential detection scheme is proposed, whose performance is almost the same as the differential space-time block coding method. However, its detection complexity only linearly increases with the number of transmitting antennas. Then, the HDD scheme is proposed, with its decision statistic being a combination of a modified OBF decision statistic and the DOBF decision statistic. In the NLOS environment, the HDD scheme is reduced to the DOBF system. In the LOS environment, a combination coefficient is obtained by mapping an SNR-related factor through a sigmoid function. The optimal parameters for the sigmoid function are determined through simulations, with which the proposed HDD scheme can achieve overall better performance than the OBF and DOBF systems.

Characterization and mitigation of urban GNSS multipath effects on smartphones

In urban environments, the code measurements of Global Navigation Satellite System (GNSS) on smartphones can be significantly degraded by presence of multipath and non-Line-Of-Sight (NLOS) effects. Extensive studies have been carried out on characterizing the quality of GNSS code measurements under different conditions, which can be used to refine the weighting model and fault detection method. However, our knowledge regarding the impacts of multipath effects on smartphones remains limited. This includes determining the dominant error source for urban GNSS on smartphones between multipath and NLOS errors, and characterizing the fault-free code measurements for pedestrians in urban canyons. To address these issues, we conducted extensive tests in a typical urban area in Hong Kong and analyzed characteristics of multipath effects. Analysis revealed that 92.5 % of code outliers stem from NLOS signals, which result in longer ranging measurements, making NLOS the dominant error source impacting GNSS performance. Further, we established that over 92 % of fault-free signals are from the opposite sky of a pedestrian, and we found that the sidewalk can be reliably determined by examining signal-to-noise ratio (SNR) measurements, eliminating the need of complex 3D building models. The identified sidewalk can be used to enhance GNSS performance in urban areas. An experiment in the urban canyon was performed in urban canyons. Results show that the accuracy can be improved from 15 m to 4.6 m when the correct sidewalk can be identified.

Design of a Multi-Standard UWB-LoRa Antenna Structure and Transceiver Board for High-Accuracy and Long-Range Localization Applications

Long-Range Wide-Area Networks (LoRaWAN) allow the transmission of data via radio link from sensors, which are potentially isolated or difficult to access, to gateways and servers that are connected to cellular networks for data processing, exchange, or relay, with low transmission power. This concept employs Long-Range (LoRa) modulation and has led to the emergence of many applications for the monitoring and tracking of objects. However, due to its characteristic of a low data rate for low-power communication, the transmission of information with LoRa technology is not suitable for the fast real-time monitoring of data. Additionally, due to its narrow bandwidth, an attempt to perform localization through the LoRa modulation technique will result in very limited accuracy because of its inability to resolve multipath problems. Thus, in this paper, we propose a multi-standard Ultra-Wide Bandwidth (UWB) and LoRa end-device that is capable of measuring location with high accuracy using UWB technology and then transmitting the location information through LoRa method to gateways and the Internet of Things Network. The results of measurements in indoor and outdoor scenarios show a UWB localization accuracy that is of sub-meter level, being between 10 and 33 cm, and a UWB range of 124 m in Line-of-Sight (LOS) and 55 m in Non-Line-of-Sight (NLOS) applications, respectively.

Efficient near-field localization aided with reconfigurable intelligent surface using geometric dilution of precision

ABSTRACT Reconfigurable intelligent surfaces (RISs) are anticipated to constitute a critical component of forthcoming communication networks due to their ability to establish controllable wireless environments. Furthermore, RIS can be leveraged to solve infeasible localization problems. This paper presents a novel adoption of the geometric dilution of precision (GDoP) analysis in the design of non-line of sight (NLoS) single anchor millimetre wave (mmWave) large RIS-aided localization in the near-field. Considering downlink transmission, the time difference of arrival (TDoA) is used to estimate the positioning by extracting the signal contribution of each RIS tile. Considering the enriched time of arrival (ToA) measurements provided by RIS, the RIS tiles involved in TDoA-based estimation should be selected properly. Therefore, the GDoP adopted in this context is to select RIS tile combinations that achieve minimum GDoP values. It has been shown via numerical simulation that the proposed design of the RIS-aided localization outperforms other state-of-the-art techniques that adopt the signal-to-noise ratio (SNR) for the selection of the RIS tiles. It is demonstrated that the proposed scheme can provide sufficient localization accuracy involving only 10% of RIS tiles, whereas the SNR method requires about (70–80) % of the tiles to approximately achieve the same accuracy.

Non-line-of-sight ultraviolet transmission coverage in non-precipitating, foggy, and rainy weather.

Sensitivity to weather conditions is the principal limitation of free-space optical communication. However, for the scattering based ultraviolet (UV) non-line-of-sight (NLOS) communication, the atmospheric scattering effect functions as an attenuation factor and potentially as a performance enhancer. To investigate the UV NLOS transmission coverage under different weather conditions, we employ the Mie Theory in conjunction with classical aerosol and hydrometeor particle models to estimate the absorption coefficient, the scattering coefficient, and the scattering phase function. We then use these atmospheric parameters combined with a range estimation model to determine the coverage of the UV NLOS communication for specified path loss. Simulation results reveal that in non-precipitating weather, poorer visibility correlates with broader coverage. In foggy conditions, the coverage range in light fog exceeds that in fog-free environments; however, as fog intensity increases, the coverage range decreases. Rain enhances the coverage range; and heavier precipitation results in a larger coverage area.

Three-Dimensional Trajectory and Resource Allocation Optimization in Multi-Unmanned Aerial Vehicle Multicast System: A Multi-Agent Reinforcement Learning Method

Unmanned aerial vehicles (UAVs) are able to act as movable aerial base stations to enhance wireless coverage for edge users with poor ground communication quality. However, in urban environments, the link between UAVs and ground users can be blocked by obstacles, especially when complicated terrestrial infrastructures increase the probability of non-line-of-sight (NLoS) links. In this paper, in order to improve the average throughput, we propose a multi-UAV multicast system, where a multi-agent reinforcement learning method is utilized to help UAVs determine the optimal altitude and trajectory. Intelligent reflective surfaces (IRSs) are also employed to reflect signals to solve the blocking problem. Furthermore, since the UAV’s onboard power is limited, this paper aims to minimize the UAVs’ energy consumption and maximize the transmission rate for edge users by jointly optimizing the UAVs’ 3D trajectory and transmit power. Firstly, we deduce the channel capacity of ground users in different multicast groups. Subsequently, the K-medoids algorithm is utilized for the multicast grouping problem of edge users based on transmission rate requirements. Then, we employ the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to learn an optimal solution and eliminate the non-stationarity of multi-agent training. Finally, the simulation results show that the proposed system can increase the average throughput by 14% approximately compared to the non-grouping system, and the MADDPG algorithm can achieve a 20% improvement in reducing the energy consumption of UAVs compared to traditional deep reinforcement learning (DRL) methods.

Curved OLED-based NLOS optical camera communications links.

In this paper, for the first time, to the best of our knowledge, we experimentally demonstrate the use of a curved organic light emitting diode (OLED) as a transmitter (Tx) in the non-line-of-sight (NLOS) optical camera communication (OCC) link for an indoor environment using a camera as a receiver. The proposed NLOS-OCC scheme is evaluated for the signal-to-noise ratio (SNR) and the reception success rates R r s under key photographic and communication parameters, including exposure times t e x p and gain values G v, as well as the transmission frequency f s and the distance L. The SNR analysis is performed using a binary classification procedure based on a Gaussian mixture model for the first time, to the best of our knowledge, for OLED-based NLOS-OCC links. We also derive and demonstrate that the effect of G v on the SNR with respect to L is minimal based on the pixel illumination model. The initial analysis suggests that, for a wall reflector-based NLOS-OCC link that is 2m long, the SNR and R r s increase by 1dB and 4% (83-87%) for f s of 600Hz, with an increase in t e x p of 1000-1500µs and G v of 25-45dB.

Research on None-Line-of-Sight/Line-of-Sight Identification Method Based on Convolutional Neural Network-Channel Attention Module.

None-Line-of-Sight (NLOS) propagation of Ultra-Wideband (UWB) signals leads to a decrease in the reliability of positioning accuracy. Therefore, it is essential to identify the channel environment prior to localization to preserve the high-accuracy Line-of-Sight (LOS) ranging results and correct or reject the NLOS ranging results with positive bias. Aiming at the problem of the low accuracy and poor generalization ability of NLOS/LOS identification methods based on Channel Impulse Response (CIR) at present, the multilayer Convolutional Neural Networks (CNN) combined with Channel Attention Module (CAM) for NLOS/LOS identification method is proposed. Firstly, the CAM is embedded in the multilayer CNN to extract the time-domain data features of the original CIR. Then, the global average pooling layer is used to replace the fully connected layer for feature integration and classification output. In addition, the public dataset from the European Horizon 2020 Programme project eWINE is used to perform comparative experiments with different structural models and different identification methods. The results show that the proposed CNN-CAM model has a LOS recall of 92.29%, NLOS recall of 87.71%, accuracy of 90.00%, and F1-score of 90.22%. Compared with the current relatively advanced technology, it has better performance advantages.

A Probabilistic Method-Based Smartphone GNSS Fault Detection and Exclusion System Utilizing PDR Step Length

A smartphone equipped with a Global Navigation Satellite System (GNSS) module can generate positional information for location-based services. However, GNSS signals are susceptible to fragility, multipath (MP), and Non-Line-Of-Sight (NLOS) interference, which can lead to a degradation in the accuracy of GNSS positioning on smartphones. Due to limitations in the smartphone’s antenna, GNSS signal strength is typically lower. Moreover, in urban areas, where smartphones rely on GNSS, MP and NLOS signals are the primary factors impeding accurate positioning. In this paper, with the goal of enhancing both the accuracy and robustness of smartphone GNSS positioning, we propose two methods. Firstly, an optimized particle filter method employing a Krill Herd Algorithm (KHA) is suggested for the integration of GNSS and Pedestrian Dead Reckoning (PDR). Secondly, a probabilistic approach is presented to identify faulty GNSS measurements using step distance information obtained from the PDR. Experimental tests were conducted using smartphones to evaluate the performance of the proposed method. The results demonstrate that both the KHA and fault detection methods effectively enhance the performance of integrated PDR and GNSS.

Assessing the Performance of a Hybrid Geolocation Algorithm Integrating FP and TOA Techniques across Diverse Environmental Conditions

This paper presents a validation study of indoor geolocation accuracies using a hybrid approach that combines fingerprinting (FP) and time of arrival (TOA) techniques. The investigation focuses on three dense environments, examining the influence of furniture density and multipath components on geolocation accuracy, particularly in non-line-of-sight (NLOS) scenarios. The results indicate that geolocation performance improves in denser environments with higher furniture density due to increased multipath components. Additionally, optimizing the hybrid method with a polygon size of 50 cm and sampling rate of 80 GHz leads to further accuracy enhancements. These findings underscore the significance of furniture density and demonstrate the effectiveness of the hybrid method in addressing NLOS challenges. The research contributes to the advancement of indoor geolocation techniques and provides valuable insights for designing precise indoor positioning systems across various applications.

Weak non-line-of-sight target echoes extraction without accumulation.

Non-line-of-sight (NLOS) technology has been rapidly developed in recent years, allowing us to visualize or localize hidden objects by analyzing the returned photons, which is expected to be applied to autonomous driving, field rescue, etc. Due to the laser attenuation and multiple reflections, it is inevitable for future applications to separate the returned extremely weak signal from noise. However, current methods find signals by direct accumulation, causing noise to be accumulated simultaneously and inability of extracting weak targets. Herein, we explore two denoising methods without accumulation to detect the weak target echoes, relying on the temporal correlation feature. In one aspect, we propose a dual-detector method based on software operations to improve the detection ability for weak signals. In the other aspect, we introduce the pipeline method for NLOS target tracking in sequential histograms. Ultimately, we experimentally demonstrated these two methods and extracted the motion trajectory of the hidden object. The results may be useful for practical applications in the future.

Robust IMU-Based Mitigation of Human Body Shadowing in UWB Indoor Positioning.

Ultra-wideband (UWB) indoor positioning systems have the potential to achieve sub-decimeter-level accuracy. However, the ranging performance degrades significantly under non-line-of-sight (NLoS) conditions. The detection and mitigation of NLoS conditions is a complex problem and has been the subject of many works over the past decades. When localizing pedestrians, human body shadowing (HBS) is a particular and specific cause of NLoS. In this paper, we present an HBS mitigation strategy based on the orientation of the body and tag relative to the UWB anchors. Our HBS mitigation strategy involves a robust range error model that interacts with a tracking algorithm. The model consists of a bank of Gaussian Mixture Models (GMMs), from which an appropriate GMM is selected based on the relative body-tag-anchor orientation. The relative orientation is estimated by means of an inertial measurement unit (IMU) attached to the tag and a candidate position provided by the tracking algorithm. The selected GMM is used as a likelihood function for the tracking algorithm to improve localization accuracy. Our proposed approach was realized for two tracking algorithms. We validated the implemented algorithms on dynamic UWB ranging measurements, which were performed in an industrial lab environment. The proposed algorithms outperform other state-of-the-art algorithms, achieving a 37% reduction of the p75 error.

Reliable Data Transmission Scheduling for UAV-Assisted Air-to-Ground Communications

Reliability is significant for unmanned aerial vehicle (UAV)-assisted air-to-ground (A2G) communications. In this letter, we address the reliability optimization problem by properly scheduling data transmissions under Non-Line-of-Sight (NLoS) channel fading. We capture the channel fading effect by the Rayleigh distribution and then formulate the A2G communication reliability from a probabilistic perspective. We propose a constrained optimization model that jointly takes into account the impacts of the aerial and ground nodes' mobility, the stochastic fading characteristics of the A2G channel, and the transmission constraints. From the model, we derive a closed-form reliability-optimal solution for data transmission scheduling and theoretically characterize the optimal reliability that is achievable. Simulation results verify our theoretical results and show the superior performance of the proposed scheduling solution over benchmark methods.