Non-line-of-sight Research Articles

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.

Performance Evaluation of LoRaWAN SX1276 Radio in Non-Line of Sight conditions

The study presents a comprehensive performance evaluation of the SX1276 radio module within LoRaWAN technology under Non-Line of Sight (NLOS) conditions. LoRaWAN's proliferation in IoT applications demands a thorough understanding of its performance in challenging environments. This study investigates NLOS scenarios, identifying factors influencing signal strength, packet loss, and latency. Finally, the performance of the network under LOS condition was compared to NLOS to shed light on NLOS challenges. Results showed that spreading factor and transmission power are the most critical parameters that influences the performance of the network. Results from the study also shows that in LOS condition the spreading factor 7 has the highest data rate but also the least range. Furthermore, the study revealed that using high spreading factor is more beneficial than high transmission power in non-line of sight conditions. Finally, by comparing both conditions, LoRaWAN nodes under LOS condition was found to have a range of up to 5 km as against 1 km at NLOS which further highlight the importance of LOS in LoRa transmissions.

PositionNet: CNN-based GNSS positioning in urban areas with residual maps

Multipath and non-light-of-sight (NLOS) reception greatly deteriorate the GNSS positioning accuracy in urban areas. Currently, most of the attempts using machine learning in this area focus on signal status prediction (LOS, multipath, NLOS). This paper exploits the capability of deep learning in multipath/NLOS mitigation. A new input feature, the single-differenced residual map is proposed, which has a high correlation with the user location and is very effective in multipath/NLOS mitigation. Combining the domain knowledge in GNSS, features of residual maps from different satellites are extracted by the proposed network and generate the heat map to indicate the user location. The proposed network can significantly improve the positioning accuracy of 84% of the epochs in the dense urban to 5-meter level. In addition, our network has a superior generalization ability, reducing the error of 90% of the epochs to 7 m level in a new scenario.

Non-line-of-sight optical camera communications based on CPWM and a convolutional neural network.

Non-line-of-sight (NLOS) optical camera communications (OCC) exhibit greater link availability and mobility than line-of-sight links, which are more susceptible to blocking and shadowing. In this work, we propose an NLOS OCC system, where the data signal is mapped into color pulse width modulation (CPWM) symbols prior to transmission using a red-, green-, and blue light-emitting diode. A convolutional-neural-network-based receiver is used to demodulate the CPWM signal. Based on experimental results, the proposed scheme effectively mitigates the effects of diffuse reflection induced intersymbol interference, resulting in an increased data transmission rate to 7.2kbps over a link span of more than 2m, which is typical for indoor applications.

Non-line-of-sight reconstruction via structure sparsity regularization.

Non-line-of-sight (NLOS) imaging allows for the imaging of objects around a corner, which enables potential applications in various fields, such as autonomous driving, robotic vision, medical imaging, security monitoring, etc. However, the quality of reconstruction is challenged by low signal-to-noise ratio (SNR) measurements. In this study, we present a regularization method, referred to as structure sparsity (SS) regularization, for denoising in NLOS reconstruction. By exploiting the prior knowledge of structure sparseness, we incorporate nuclear norm penalization into the cost function of the directional light-cone transform (DLCT) model for the NLOS imaging system. This incorporation effectively integrates the neighborhood information associated with the directional albedo, thereby facilitating the denoising process. Subsequently, the reconstruction is achieved by optimizing a directional albedo model with SS regularization using the fast iterative shrinkage-thresholding algorithm (FISTA). Notably, the robust reconstruction of occluded objects is observed. Through comprehensive evaluations conducted on both synthetic and experimental datasets, we demonstrate that the proposed approach yields high-quality reconstructions, surpassing the state-of-the-art reconstruction algorithms, especially in scenarios involving short exposure and low-SNR measurements.

Energy-Efficient Emerging Optical Wireless Links

In recent years, the tremendous increase in data traffic carried by wireless communication networks has generated the urgent need for establishing more energy-efficient wireless communication systems. Recent advances in semiconductor and light devices have triggered remarkable research interest to the development of these optical wireless communication (OWC) links. Among them, free-space optical (FSO) links and, more recently, ultraviolet links which operate within the (UV-C) spectral band, have been considered as prime candidates to create both high speed and power effective line-of-sight (LOS) and non-light-of-sight (NLOS) free-air communication links, respectively. Moreover, transdermal optical wireless (TOW) links for telemetry with medical implants minimize the expense of power for the implant. In the current review, a background on the energy efficiency challenges in wireless communication is presented. Each of these OWC technologies is mainly discussed in terms of key energy consumption requirements and major limiting factors that affect their power performance. Energy-efficient modulation formats as well as other powerful techniques for performance enhancement such as diversity and relaying are assessed. The survey is concluded with a discussion regarding their future energy consumption requirements and trends.

Improved Indoor Positioning Model Based on UWB/IMU Tight Combination with Double-Loop Cumulative Error Estimation

With the increasing applications of UWB indoor positioning technologies in industrial areas, to further enhance the positioning precision, the UWB/IMU combination method (UICM) has been considered as one of the most effective solutions to reduce non-line-of-sight (NLOS) errors. However, most conversional UICMs suffer from a high probability of positioning failure due to uncontrollable and cumulative errors from inertial measuring units (IMU). Hence, to address this issue, we improved the extended Kalman filter (EKF) algorithm of an indoor positioning model based on UWB/IMU tight combination with a double-loop error self-correction. Compared with conventional UICMs, this improved model consists of new modules for fixing time desynchronization, optimizing the threshold setting for UWB ranging, data fusion in NLOS, and double-loop error estimation, sequentially. Further, systematic error controllability analysis proved that the proposed model could satisfy the controllability of UWB indoor positioning systems. To validate this improved UICM, inevitable obstacles and atmospheric interferences were regarded as Gaussian white noises to verify its environmental adaptability. Finally, the experimental results showed that this proposed model outperformed the state-of-the-art UWB-based positioning models with a maximum deviation of 0.232 m (reduced by 83.93% compared to a pure UWB model and 43.14% compared to the conventional UWB/IMU model) and standard deviation of 0.09981 m (reduced by 88.35% compared to a pure UWB model and 22.21% compared to the conventional UWB-IMU model).

Indoor Non‐Line‐of‐Sight Millimeter‐Wave Coverage Enhancements by Field‐Reorganizable Passive Digital Coding Metasurfaces

Digital metasurfaces define a novel methodology for metasurface designs by adopting discrete coding meta‐atoms to engineer electromagnetic waves in programmable ways. Herein, a novel field‐reorganizable digital metasurface (FRDM) that can be used to enhance the indoor non‐line‐of‐sight (NLOS) millimeter‐wave (mmWave) signal coverage is presented. The passive binary supercells which can be reorganized with an in situ optimization characteristic to deflect the incoming mmWaves into the blind area with adjustable angles are proposed. The indoor L‐shaped corridor signal coverage simulated by the ray‐tracing technique confirms that the NLOS blind area is effectively communicated using the passive FRDM. Practical environment experiments using FRDMs with different coding sequences are conducted, indicating that the averaged signal intensity is increased by over 10 dB in the NLOS blind area in a wide operating frequency band from 26 to 30 GHz by reorganizing the passive digital metasurfaces. The results suggest that the proposed FRDM enabled by the reorganizable binary supercells is a highly flexible, low‐cost, and extensible solution for B5G and 6G mmWave wireless communications.

Research on Autonomous and Collaborative Deployment of Massive Mobile Base Stations in High-Rise Building Fire Field.

High-rise building fires pose a serious threat to the lives and property safety of people. The lack of reliable and accurate positioning means is one of the main difficulties faced by rescuers. In the absence of prior knowledge of the high-rise building fire environment, the coverage deployment of mobile base stations is a challenging problem that has not received much attention in the literature. This paper studies the problem of the autonomous optimal deployment of base stations in high-rise building fire environments based on a UAV group. A novel problem formulation is proposed that solves the non-line-of-sight (NLOS) positioning problem in complex and unknown environments. The purpose of this paper is to realize the coverage and deployment of mobile base stations in complex and unknown fire environments. The NLOS positioning problem in the fire field environment is turned into the line-of-sight (LOS) positioning problem through the optimization algorithm. And there are more than three LOS base stations nearby at any point in the fire field. A control law which is formulated in a mathematically precise problem statement is developed that guarantees to meet mobile base stations' deployment goals and to avoid collision. Finally, the positioning accuracy of our method and that of the common method were compared under many different cases. The simulation result showed that the positioning error of a simulated firefighter in the fire field environment was improved from more than 10 m (the positioning error of the traditional method) to less than 1 m.

Dual-Propagation-Feature Fusion Enhanced Neural CSI Compression for Massive MIMO

Due to the ability of feature extraction, deep learning (DL)-based methods have been recently applied to channel state information (CSI) compression feedback in massive multiple-input multiple-output (MIMO) systems. Existing DL-based CSI compression methods are usually effective in extracting a certain type of features in the CSI. However, the CSI usually contains two types of propagation features, i.g., non-line-of-sight (NLOS) propagation-path feature and dominant propagation-path feature, especially in channel environments with rich scatterers. To fully extract the both propagation features and learn a dual-feature representation for CSI, this paper proposes a dual-feature-fusion neural network (NN), referred to as DuffinNet. The proposed DuffinNet adopts a parallel structure with a convolutional neural network (CNN) and an attention-empowered neural network (ANN) to respectively extract different features in the CSI, and then explores their interplay by a fusion NN. Built upon this proposed DuffinNet, a new encoder-decoder framework is developed, referred to as Duffin-CsiNet, for improving the end-to-end performance of CSI compression and reconstruction. To facilitate the application of Duffin-CsiNet in practice, this paper also presents a two-stage approach for codeword quantization of the CSI feedback. Besides, a transfer learning-based strategy is introduced to improve the generalization of Duffin-CsiNet, which enables the network to be applied to new propagation environments. Simulation results illustrate that the proposed Duffin-CsiNet noticeably outperforms the existing DL-based methods in terms of reconstruction performance, encoder complexity, and network convergence, validating the effectiveness of the proposed dual-feature fusion design.

Clutter Effect Investigation on Co-Polarized Chipless RFID Tags and Mitigation Using Cross-Polarized Tags, Analytical Model, Simulation, and Measurement.

Chipless radio frequency identification (RFID) technology is expected to replace barcode technology due to its ability to read in non-line-of-sight (NLOS) situations, long reading range, and low cost. Currently, there is extensive research being conducted on frequency-coded (FC) co-polarized radar cross-section (RCS)-based tags, which are widely used. However, detecting co-polarized chipless RFID tags in cluttered environments is still a challenge, as confirmed by measuring two co-polarized tags in front of a perfect metal reflector (30.5cm×22.5cm). To address this challenge, a realistic mathematical model for a chipless RFID system has been developed that takes into account the characteristics of the reader and the tag, as well as reflections from cluttered objects. This extensive mathematical model developed for linear chipless RFID systems in clutter scenarios holds the potential to greatly assist researchers in their exploration of RCS-based tags. By relying solely on simulations, this model provides a tool to effectively analyze and understand RCS-based tags, ultimately simplifying the process of generating more authentic tag designs. This model has been simulated and verified with measurement results by placing a single flat metal reflector behind two co-polarized one-bit designs: a dipole array tag and a square patch tag. The results showed that the interfering signal completely overlaps the ID of the co-polarized tag, severely limiting its detectability. To solve this issue, the proposed solution involves reading the tag in cross-polarization mode by etching a diagonal slot in the square patch tag. This proposed tag provides high immunity to the environment and can be detected in front of both dielectric and metallic objects.

Improved Strong Tracking Cubature Kalman Filter for UWB Positioning.

For the problems of Non-Line-of-Sight (NLOS) observation errors and inaccurate predictive dynamics model in wireless ultra-wideband (UWB) positioning systems, an improved strong tracking cubature Kalman filter (ISTCKF) positioning algorithm is proposed in this paper. The main idea of the algorithm is as follows. First, the observations are reconstructed based on the weighted positioning results obtained from the predictive dynamics model and the least squares algorithm. Second, the difference in statistical properties between the observation noise and the NLOS errors is utilized to identify the NLOS observations by the corresponding judgment statistics obtained from the operation between the original observations and the reconstructed observations. The main positioning error of the UWB positioning system at the current moment is then judged by the NLOS identification results, and the corresponding fading factors are calculated according to the judgment results. Finally, the corresponding ISTCKF is constructed based on the fading factors to mitigate the main positioning error and obtain accurate positioning result in the UWB positioning system. In this paper, the reconstructed observations mitigate the observation noise in the original observation, and then the ISTCKF mitigates the main errors in the UWB positioning system. The experimental results show that the ISTCKF algorithm reduces the positioning error by 55.2%, 32.3% and 28.9% compared with STCKF, ACKF and RSTCKF, respectively. The proposed ISTCKF algorithm significantly improves the positioning accuracy and stability of the UWB system.

5G Vs Wi-Fi Indoor Positioning: A Comparative Study

Sensing location information in indoor scenes requires a high accuracy and is a challenging task, mainly because of multipath and NLoS (non-line-of-sight) propagation. GNSS signals cannot penetrate well in indoor environment. Satellite-based navigation and positioning systems cannot therefore be used for indoor positioning.. Other technologies have been suggested for indoor usage, among them, Wi-Fi (802.11) and 5G NR (New Radio). The primary aim of this study is to discuss the advantages and drawbacks of 5G and Wi-Fi positioning techniques for indoor localization.

Efficient Multi-Hop Wireless Power Transfer for the Indoor Environment.

With the rapid development of the Internet of Things (IoT) and wireless sensor networks (WSN), the modern world requires advanced solutions for the wireless powering of low-power autonomous devices. The present study addresses the wireless power transfer (WPT) efficiency problem by exploiting a multi-hop concept-based technique to increase the received power at the end sensor node (ESN). The current work adopts efficient multi-hop technology from the communications field to examine its impact on WPT performance. The investigation involves power transfer modeling and experimental measurements in a sub-GHz frequency range, chosen for being capable of providing a greater distance to transmit power. The paper proposes a multi-hop (MH) WPT concept based on signal amplification and demonstrates the fabricated multi-hop node (MHN) prototype. The experimental verification of the MHN is performed in the laboratory environment. The present paper examines two WPT scenarios: line-of-sight (LoS) and non-line-of-sight (NLoS). The turn-on angle of 90 degrees on MHN is used for the NLoS case. The received power and RF-DC converted voltage on the ESN are measured for all investigated scenarios. Moreover, the paper proposes an efficient simulation approach for the performance evaluation of MH WPT technology, providing an opportunity to analyze and optimize wireless sensor nodes' spatial distribution to increase the received power.

Underwater Wireless Sensor Network-Based Localization Method under Mixed Line-of-Sight/Non-Line-of-Sight Conditions

Source localization in underwater sensor networks (UWSNs) presents complex challenges due to sensor nodes drift caused by ocean currents, non-line-of-sight (NLOS) propagation resulting from underwater multipath effects, and environmental noise. This paper proposes a practical and innovative algebraic solution based on the time difference of arrival (TDOA) for source localization in shallow seas. The proposed solution effectively addresses the issues arising from sensor position errors and multipath effects by incorporating the sea-surface reflection non-line-of-sight (SNLOS) link and optimizing the algorithm, thereby significantly improving positioning accuracy. The core concept of the method involves utilizing the weighted least squares algorithm to obtain an initial estimate of the source position, followed by direct estimation of the bias and subsequent refinement of the solution. In contrast to traditional closed-form solutions, this method avoids the introduction of intermediate parameters and directly handles the estimated bias from the previous step. Even when only considering the line-of-sight (LOS) link, the proposed solution achieves precise localization with a minimal number of sensors. Theoretical analysis demonstrates that the solution can achieve the Cramér–Rao lower bound (CRLB) accuracy under low noise conditions, and simulation results validate the superior performance of the proposed solution.

LoRaWAPS: A Wide-Area Positioning System Based on LoRa Mesh

The positioning task of the Internet of Things (IoT) for outdoor environments requires that the node devices meet the requirements of low power consumption, long endurance, and low cost and that the positioning system can achieve high-precision positioning and wide-area coverage. Considering that traditional IoT positioning technology cannot balance the cost, energy consumption, and positioning performance well, a Wide-Area Positioning System Based on Long Range Mesh (LoRaWAPS), which is a low-cost and low-power outdoor positioning system with multi-anchor wireless mesh networking and multi-dimensional data fusion, is designed in this paper. To meet the need for a positioning system, a low-power consumption and high-reliability LoRa Mesh protocol is proposed. Aiming at the problem that the accuracy of LoRa ranging is easily affected by the non-line-of-sight (NLOS) path propagation of signals, a distance estimation algorithm based on the fusion of time of flight (TOF) and received signal strength indicator (RSSI) multi-sampling data is proposed. Furthermore, a position estimation algorithm is designed to minimize the posteriori RSSI error for multi-anchor cooperative estimation scenarios. Furthermore, the prototype of LoRaWAPS is built and tested in the campus environment. The experimental results show that the proposed system can provide reliable location service with low power and low cost.

Route Optimization of Unmanned Aerial Vehicle Sensors for Localization of Wireless Emitters in Outdoor Environments

Localization methods of unknown emitters are used for the monitoring of illegal radio waves. The localization methods using ground-based sensors suffer from a degradation of localization accuracy in environments where the distance between the emitter and the sensor is non-line-of-sight (NLoS). Therefore, research is being conducted to improve localization accuracy by utilizing Unmanned Aerial Vehicles (UAVs) as sensors to ensure a line-of-sight (LoS) condition. However, UAVs can fly freely over the sky, making it difficult to optimize flight paths based on particle swarm optimization (PSO) for efficient and accurate localization. This paper examines the optimization of UAV flight paths to achieve highly efficient and accurate outdoor localization of unknown emitters via two approaches, a circular orbit and free-path trajectory, respectively. Our numerical results reveal the improved localization estimation error performance of our proposed approach. Particularly, when evaluating at the 90th percentile of the error’s cumulative distribution function (CDF), the proposed approach can reach an error of 28.59 m with a circular orbit and 12.91 m with a free-path orbit, as compared to the conventional fixed sensor case whose localization estimation error is 55.02 m.

The Joint Channel Coding and Pre-Distortion Technique on the USRP-Based MIMO-OFDM System

Modern wireless communication systems use orthogonal frequency division multiplexing (OFDM), a multi-carrier modulation method that resists multipath channels and provides bandwidth efficiency. OFDM is generally used with a Multiple-Input Multiple-Output (MIMO) system to boost diversity gain and channel capacity. MIMO-OFDM has several advantages, but its high PAPR value is a drawback. A non-linear high-power amplifier (HPA) can distort signals with high PAPR values. This issue can be resolved by employing predistortion, which compensates for nonlinear HPA. In addition to PD, channel coding can be used to improve the quality of systems with high PAPR values by adding redundant bits to the bits to be sent. In this paper, we report the experimental evaluations of the joint channel coding and pre-distortion (PD) technique on a 2x2 MIMO OFDM system using USRP hardware. The experiments are conducted in two scenarios: line-of-sight (LOS) and nonline-of-sight (NLOS) scenarios. The channel coding used in this scenario is convolutional code with code rates of 1/2, 2/3, and 3/4. From the results of the experiment, it can be seen that the system that uses PD combined with the convolution code produces better performance in the LOS and NLOS scenarios compared to the system without PD. In the LOS scenario, the use of PD can improve the SNR value of code rates 1/2, 2/3, and 3/4 by approximately 58.74%, 75.97%, and 96.20%. In the NLOS scenario, the use of PD can improve the SNR value of code rates 1/2, 2/3, and 3/4 by about 60.71%, 73.59%, and 71.84%. The measurement of the LOS scenario gives a better SNR value than the NLOS scenario, with a maximum SNR value of 30.86 dB, while the maximum SNR value of the NLOS scenario is 30.23 dB. This happened because the LOS scenario suffered minimal multipath fading compared to the NLOS scenario

A Visible Light 3D Positioning System for Underground Mines Based on Convolutional Neural Network Combining Inception Module and Attention Mechanism

To improve the accuracy of personnel positioning in underground coal mines, in this paper, we propose a convolutional neural network (CNN) three-dimensional (3D) visible light positioning (VLP) system based on the Inception-v2 module and efficient channel attention mechanism. The system consists of two LEDs and four photodetectors (PDs), with the four PDs on the miner’s helmet. Considering the height fluctuation of PD and the impact of wall reflection on the received light power, we adopt the Inception module to perform a multi-scale extraction of the features of the received light power, thus solving the limitation of the single-scale convolution kernel on the positioning accuracy. In order to focus on the information that is more critical to positioning among the numerous input features, giving different features of the optical power data corresponding weights, we use an efficient channel attention mechanism to make the positioning model more accurate. The simulation results show that the average positioning error of the system was 1.63 cm in the space of 6 m × 3 m × 3.6 m when both the line-of-sight (LOS) and non-line-of-sight (NLOS) links were considered, with 90% of the localization errors within 4.55 cm. During the experimental stage, the average positioning error was 11.12 cm, with 90% of the positioning errors within 28.75 cm. These show that the system could achieve centimeter-level positioning accuracy and meet the requirements for underground personnel positioning in coal mines.