Non-line-of-sight Research Articles

Curvature Regularization for Non-Line-of-Sight Imaging From Under-Sampled Data.

Non-line-of-sight (NLOS) imaging aims to reconstruct the three-dimensional hidden scenes by using time-of-flight photon information after multiple diffuse reflections. The under-sampled scanning data can facilitate fast imaging. However, the resulting reconstruction problem becomes a serious ill-posed inverse problem, the solution of which is highly likely to be degraded due to noises and distortions. In this paper, we propose novel NLOS reconstruction models based on curvature regularization, i.e., the object-domain curvature regularization model and the dual (signal and object)-domain curvature regularization model. In what follows, we develop efficient optimization algorithms relying on the alternating direction method of multipliers (ADMM) with the backtracking stepsize rule, for which all solvers can be implemented on GPUs. We evaluate the proposed algorithms on both synthetic and real datasets, which achieve state-of-the-art performance, especially in the compressed sensing setting. Based on GPU computing, our algorithm is the most effective among iterative methods, balancing reconstruction quality and computational time.

Urban skies safeguarded: innovative drone detection with programmable metasurface periscope

Programmable metasurfaces (PMSs) exhibit great potentials in target detection techniques, because they can take actions to change channel propagation characteristics which introduces further degrees of freedom for system optimizations. However, responses of most traditional PMSs are sensitive to incident angles of impinging electromagnetic waves, resulting in a failure of angular estimation to dynamic targets coming from different directions. Herein, by proposing a fully resonant structure and introducing a mode-alignment technology, we report an isotropic angle-insensitive PMS whose phase response is stable with respect to different incident angles in both elevation- and azimuth-planes. A radar scheme that uses such a PMS as a periscope is also demonstrated, to detect drones in a non-line-of-sight (N-LOS) scenario which usually happens in an urban environment. Our proposed scheme enables those targets even falling in shadow areas caused by high buildings to be successfully detected and tracked, which shows promising potentials in N-LOS target detections.

Enhanced 3D Outdoor Positioning Method Based on Adaptive Kalman Filter and Kernel Density Estimation for 6G Wireless System

The implementation of accurate positioning methods in both line-of-sight (LOS) and non-line-of-sight (NLOS) environments has been emphasized for seamless 6G application services. In LOS environments with unobstructed paths between the transmitter and receiver, accurate tracking essential for seamless 6G services is achievable. However, accurate three-dimensional (3D) outdoor positioning has been challenging to achieve in NLOS environments where positioning accuracy may be severely degraded. In this paper, a novel 3D outdoor positioning method considering both LOS and NLOS environments is proposed. Considering the practical positioning systems, the data received from satellites often contain null values and outliers. Thus, a kernel density estimation (KDE)-based outlier removal method is used for effectively detecting the null values and outliers through temporal correlation analysis. A dilution of precision-based adaptive Kalman filter (DOP-AKF) is proposed to mitigate the effects of an NLOS environment. In the proposed method, the DOP-AKF can optimize the performance of the 3D positioning system that dynamically adapts to complex environments. Experimental results show that the proposed method can improve 3D positioning accuracy by up to 18.84% compared to conventional methods. Therefore, the proposed approach can be suggested as a promising solution for 3D outdoor positioning in 6G wireless systems.

A reliable NLOS error identification method based on LightGBM driven by multiple features of GNSS signals

In complicated urban environments, Global Navigation Satellite System (GNSS) signals are frequently affected by building reflection or refraction, resulting in Non-Line-of-Sight (NLOS) errors. In severe cases, NLOS errors can cause a ranging error of hundreds of meters, which has a substantial impact on the precision and dependability of GNSS positioning. To address this problem, we propose a reliable NLOS error identification method based on the Light Gradient Boosting Machine (LightGBM), which is driven by multiple features of GNSS signals. The sample data are first labeled using a fisheye camera to classify the signals from visible satellites as Line-of-Sight (LOS) or NLOS signals. We then analyzed the sample data to determine the correlation among multiple features, such as the signal-to-noise ratio, elevation angle, pseudorange consistency, phase consistency, Code Minus Carrier, and Multi-Path combined observations. Finally, we introduce the LightGBM model to establish an effective correlation between signal features and satellite visibility and adopt a multifeature-driven scheme to achieve reliable identification of NLOSs. The test results show that the proposed method is superior to other methods such as Extreme Gradient Boosting (XGBoost), in terms of accuracy and usability. The model demonstrates a potential classification accuracy of approximately 90% with minimal time consumption. Furthermore, the Standard Point Positioning results after excluding NLOSs show the Root Mean Squares are improved by 47.82%, 56.68%, and 36.68% in the east, north, and up directions, respectively, and the overall positioning performance is significantly improved.

Codesign of transmit waveform and reflective beamforming for active reconfigurable intelligent surface-aided MIMO ISAC system

This article discusses the active reconfigurable intelligent surface (ARIS)-aided integrated sensing and communication (ISAC) system for non-line-of-sight (NLoS) target sensing in cluttered environments while performing multi-user communication. To optimize sensing and communication performance simultaneously, we jointly design the shared transmit waveform, ARIS reflection coefficients and radar receive filter by using the multi-user interference and the reciprocal of radar output signal-to-interference-plus-noise ratio as metrics. Limited by practical requirements, the transmit waveform suffers from constant modulus or total energy constraints and the ARIS is subject to both maximum power and amplification gain constraints. Based on these considerations, the proposed codesign is formulated into a nonconvex constrained fractional function minimization problem. To tackle it effectively, we first translate the fractional objective into an integral form by employing Dinkelbach transform and then propose an alternating optimization-based algorithm, where the transmit waveform and ARIS reflection coefficients are respectively optimized by the customized algorithms based on the consensus alternating direction method of multipliers, and the receive filter has a closed-form optimal solution. Numerical results demonstrate that the ARIS-aided ISAC concurrently achieve superior NLoS sensing and communication performance to passive reconfigurable intelligent surface-aided and traditional ISACs in cluttered environments, regardless of waveform constraints and sensing-communication trade-off factor.

Machine Learning-Based Environment-Aware GNSS Integrity Monitoring for Urban Air Mobility

The increasing deployment of unmanned aerial vehicles (UAVs) in urban air mobility (UAM) necessitates robust Global Navigation Satellite System (GNSS) integrity monitoring that can adapt to the complexities of urban environments. The traditional integrity monitoring approaches struggle with the unique challenges posed by urban settings, such as frequent signal blockages, multipath reflections, and Non-Line-of-Sight (NLoS) receptions. This study introduces a novel machine learning-based GNSS integrity monitoring framework that incorporates environment recognition to create environment-specific error models. Using a comprehensive Hardware-in-the-Loop (HIL) simulation setup, extensive data were generated for suburban, urban, and urban canyon environments to train and validate the models. The proposed Natural Gradient Boosting Protection Level (NGB-PL) method, leveraging the uncertainty prediction capabilities of the NGB algorithm, demonstrated superior performance in estimating protection levels compared to the classical methods. The results indicated that environment-specific models significantly enhanced both accuracy and system availability, particularly in challenging urban scenarios. The integration of environment recognition into the integrity monitoring framework allows the dynamic adaptation to varying environmental conditions, thus substantially improving the reliability and safety of UAV operations in urban air mobility applications. This research offers a novel protection level (PL) estimation method and a framework tailored to GNSS integrity monitoring for UAM, which enhances the availability with narrower PL bound gaps without yielding higher integrity risks.

Optimizing resource allocation for enhanced urban connectivity in LEO-UAV-RIS networks

Sixth-generation (6G) communication advancements target massive connectivity, ultra-reliable low-latency communication (URLLC), and high data rates, essential for IoT applications. Yet, in natural disasters, particularly in dense urban areas, 6G quality of service (QoS) can falter when terrestrial networks—such as base stations—become unavailable, unstable, or strained by high user density and dynamic environments. Additionally, high-rise buildings in smart cities contribute to signal blockages. To ensure reliable, high-quality connectivity, integrating low-Earth Orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and reconfigurable intelligent surfaces (RIS) into a multilayer (ML) network offers a solution: LEO satellites provide broad coverage, UAVs reduce congestion with flexible positioning, and RIS enhances signal quality. Despite these benefits, this integration brings challenges in resource allocation, requiring path loss models that account for both line-of-sight (LOS) and non-line-of-sight (NLOS) links. To address these, a joint optimization problem is formulated focusing on resource distribution fairness. Given its complexity, a framework is proposed to decouple the problem into subproblems using the block coordinate descent (BCD) method. These subproblems include UAV placement optimization, user association, subcarrier allocation via orthogonal frequency division multiple access (OFDMA), power allocation, and RIS phase shift control. OFDMA efficiently manages shared resources and mitigates interference. This iterative approach optimizes each subproblem, ensuring convergence to a locally optimal solution. Additionally, we propose a low-complexity solution for RIS phase shift control, proving its feasibility and efficiency mathematically. The numerical results demonstrate that the proposed scheme achieves up to 43.5% higher sum rates and 80% lower outage probabilities compared to the schemes without RIS. The low complexity solution for RIS optimization achieves performance within 1.8% of the SDP approach in terms of the sum rate. This model significantly improves network performance and reliability, achieving a 16.3% higher sum rate and a 44.4% reduction in outage probability compared to joint optimization of SAT-UAV resources. These findings highlight the robustness and efficiency of the ML network model, making it ideal for next-generation communication systems in high-density urban environments.

Fingerprinting‐Based Indoor Localization in a 3 × 3 Meter Grid Using OFDM Signals at Sub‐6 GHz

ABSTRACTAccurately determining the indoor location of mobile devices has garnered significant interest due to the complex challenges posed by non‐line‐of‐sight (NLOS) propagation and multipath effects. To address this challenge, this paper proposes a new approach to indoor positioning that utilises channel state information (CSI) and machine learning (ML) techniques to improve accuracy. The proposed method extracts the amplitude and phase differences of the subcarriers from the CSI data to create fingerprints. ML algorithms and network architecture are utilised to train the CSI data from two antennas, in the form of phase and amplitude. Experiments conducted in a standard indoor environment demonstrate the effectiveness of the proposed method.

Tightly Coupled LIDAR/IMU/UWB Fusion via Resilient Factor Graph for Quadruped Robot Positioning

Continuous accurate positioning in global navigation satellite system (GNSS)-denied environments is essential for robot navigation. Significant advances have been made with light detection and ranging (LiDAR)-inertial measurement unit (IMU) techniques, especially in challenging environments with varying lighting and other complexities. However, the LiDAR/IMU method relies on a recursive positioning principle, resulting in the gradual accumulation and dispersion of errors over time. To address these challenges, this study proposes a tightly coupled LiDAR/IMU/UWB fusion approach that integrates an ultra-wideband (UWB) positioning technique. First, a lightweight point cloud segmentation and constraint algorithm is designed to minimize elevation errors and reduce computational demands. Second, a multi-decision non-line-of-sight (NLOS) recognition module using information entropy is employed to mitigate NLOS errors. Finally, a tightly coupled framework via a resilient mechanism is proposed to achieve reliable position estimation for quadruped robots. Experimental results demonstrate that our system provides robust positioning results even in LiDAR-limited and NLOS conditions, maintaining low time costs.

Reconfigurable Intelligent Surfaces Assisted NLOS Radar Anti Jamming Using Deep Reinforcement Learning

The complexity of the radar environment increases with technological advancement, especially when considering the difficulties presented by repeating jammers. These jammers can impede radar detection, especially when they create false targets in non-line-of-sight (NLOS) situations. This study focuses on optimizing the phase shifts of Reconfigurable Intelligent Surfaces (RIS) to address the problem of NLOS between a target and radar for detection in order to address these NLOS issues. Specifically, we investigate RIS phase shift optimization using a Genetic Algorithm (GA) to address the challenges posed by repeating jammers across various dynamic scenarios. Our objective is to increase the radar system’s ability to detect actual targets in non-LOS scenarios when repeater jammers are present in the environment. According to the experimental results, this method offers a practical way to mitigate the effects of repeater jammers by improving radar detection performance in NLOS environments.

A Design of NLOS Communication Scheme Based on SC-FDE with Cyclic Suffix for UAV Payload Communication

Non-line-of-sight (NLOS) communication with severe loss always leads to performance degradation in unmanned aerial vehicle (UAV) payload communication. In this paper, a UAV NLOS communication scheme based on single-carrier frequency domain equalization with cyclic prefix and cyclic suffix (CP/CS-SC-FDE) is designed. First, the reasons behind the generation of later intersymbol interference (LISI) in UAV NLOS communication are investigated. Then, the frame structure of conventional single-carrier frequency domain equalization with cyclic prefix (CP-SC-FDE) is improved, and the UAV NLOS communication frame structure based on cyclic prefix (CP) and cyclic suffix (CS) is designed. Furthermore, a channel estimation algorithm applicable to this scheme is proposed. The numerical results show that this UAV communication scheme can eliminate intersymbol interference (ISI) in NLOS communication. Compared with the conventional CP-SC-FDE system, this scheme can achieve excellent performance in the Rayleigh channel and other standard NLOS channels. In the CP/CS-SC-FDE system, the BER result is similar to that under ideal synchronization.

TDOA-AOA Localization Algorithm for 5G Intelligent Reflecting Surfaces

5G positioning technology has become deeply integrated into daily life. However, in wireless signal propagation environments, there may exist non-line-of-sight (NLOS) conditions, which lead to signal blockage and subsequently hinder the provision of positioning services. To address this issue, this paper proposes an intelligent reflecting surface (IRS) NLOS time difference of arrival–angle of arrival (TDOA-AOA) localization (INTAL) algorithm. First, the algorithm constructs a system model for 5G IRS localization, effectively overcoming the challenges of positioning in NLOS paths. Then, by applying the multiple signal classification algorithm to estimate the time delay and angle, and using the Chan algorithm to obtain the user’s estimated coordinates, an optimization problem is formulated to minimize the distance between the estimated and actual coordinates. The tent–snake optimization algorithm is employed to solve this optimization problem, thereby reducing localization errors. Finally, simulations demonstrate that the INTAL algorithm outperforms the snake optimization (SO) algorithm and the gray wolf optimization (GWO) algorithm under the same conditions, reducing the localization error by 56% and 60% on average, respectively. Additionally, when the signal-to-noise ratio is 30 dB, the localization error of the INTAL algorithm is only 0.2968 m, while the errors for the SO and GWO algorithms are 0.6733 m and 0.7398 m, respectively. This further proves the significant improvement of the algorithm in terms of localization accuracy.

Real-time non-line-of-sight computational imaging using spectrum filtering and motion compensation.

Non-line-of-sight (NLOS) imaging aims at recovering the shape and albedo of hidden objects. Despite recent advances, real-time video of complex and dynamic scenes remains a major challenge owing to the weak signal of multiply scattered light. Here we propose and demonstrate a framework of spectrum filtering and motion compensation to realize high-quality NLOS video for room-sized scenes. Spectrum filtering leverages a wave-based model for denoising and deblurring in the frequency domain, enabling computational image reconstruction with a small number of sampling points. Motion compensation tailored with an interleaved scanning scheme can compute high-resolution live video during the acquisition of low-quality image sequences. Together, we demonstrate live NLOS videos at 4 fps for a variety of dynamic real-life scenes. The results mark a substantial stride toward real-time, large-scale and low-power NLOS imaging and sensing applications.

An enhanced mobile localization algorithm integrating multiple AUKF models for mixed indoor environments

Abstract The positioning technology based on ultra-wideband ranging has been widely applied in the field of indoor positioning due to its excellent localization capabilities. However, mixed line-of-sight (LOS) and non-LOS (NLOS) indoor environments severely constrain positioning accuracy. To address this issue, we propose an innovative algorithm based on the adaptive unscented Kalman filter (AUKF) and interactive multiple model (IMM), designed to significantly enhance positioning accuracy in mixed indoor environments by mitigating the impact of NLOS errors and inaccurate process noise. Firstly, recognizing the distinct characteristics of ranging errors in indoor environments, we develop LOS and NLOS ranging models separately. Based on these models, the unscented Kalman filters are constructed for LOS and NLOS environments to accurately simulate the mixed LOS/NLOS indoor environments. Secondly, determining the statistical characteristics of process noise is challenging, often leading to degraded filter performance. We address this issue by proposing an environment-based AUKF algorithm, which significantly enhances the robustness and accuracy of the positioning system. Finally, the environment-based AUKFs are integrated into the IMM framework to constrain NLOS errors and achieve precise positioning effectively. Simulations, open-source dataset validation and experimental results demonstrate that the proposed algorithm significantly enhances the accuracy and stability of mobile target positioning in mixed LOS/NLOS indoor environments.

Deep Learning for Fast and Reliable Initial Access in AI-Driven 6G mmWave Networks

We present DeepIA, a deep neural network (DNN) framework for fast and reliable initial access (IA) for artificial intelligence (AI)-driven 6G millimeter wave (mmWave) networks. DeepIA reduces the beam sweep time compared to a conventional exhaustive search-based IA process by utilizing only a subset of the available beams. DeepIA maps received signal strengths (RSSs) obtained from a subset of beams to the beam that is best oriented to the receiver. In both line of sight (LoS) and non-line of sight (NLoS) conditions, DeepIA reduces the IA time and outperforms the conventional IA&#x0027;s beam prediction accuracy. We show that DeepIA&#x0027;s accuracy saturates with the number of beams used, and also depends on the particular choice of the beams used. The choice of beams that are selected is consequential and improves accuracy by upto upto <inline-formula><tex-math notation="LaTeX">$35\%$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$70\%$</tex-math></inline-formula> in NLoS and LoS channels. We find that, averaging multiple RSS snapshots further reduces the number of beams needed and achieves more than <inline-formula><tex-math notation="LaTeX">$95\%$</tex-math></inline-formula> accuracy in both LoS and NLoS conditions. We introduce interference into our models to understand impact on performance. Finally, we evaluate the beam prediction time of DeepIA through embedded hardware implementation and show the improvement over the baseline approach.

Deep learning with synthetic data for wireless NLOS positioning with a single base station

Traditional wireless positioning methods exhibit limitations in the face of signal distortions prevalent in non-line-of-sight (NLOS) conditions, especially in the case of a single base station (BS). Moreover, the adoption of deep learning (DL) methodologies has lagged behind, largely due to the challenges associated with generating real-world datasets. In this paper, we present a comprehensive approach leveraging DL over large-scale synthetic wireless datasets (the recent WAIR-D in this case, which was co-produced by Huawei) to overcome such challenges and address the case of single-BS NLOS positioning. The aim of the paper is to practically explore the extent to which synthetic wireless datasets can help to achieve the positioning objectives. Towards this direction, we develop a map-based representation of a radio link, demonstrating its synergistic effect with feature-based representations in MLPs. Furthermore, we introduce a UNet-based neural model which incorporates input maps and radio link representations and generates as output a heatmap of potential device positions. This model achieves an 11.3-meter RMSE and 76.5% prediction accuracy on NLOS examples (1.5-meter, 99.9% for LOS) assuming perfect information, surpassing the MLP baseline by 47%. Finally, we provide further insights into the model’s ability to predict top device positions, the characteristics of predicted heatmaps as indicators of confidence, and the crucial role of map availability and radio path angles in model performance, thus revealing an unconventional perspective on incorrect predictions.

Influence of Target Surface BRDF on Non-Line-of-Sight Imaging.

The surface material of an object is a key factor that affects non-line-of-sight (NLOS) imaging. In this paper, we introduce the bidirectional reflectance distribution function (BRDF) into NLOS imaging to study how the target surface material influences the quality of NLOS images. First, the BRDF of two surface materials (aluminized insulation material and white paint board) was modeled using deep neural networks and compared with a five-parameter empirical model to validate the method's accuracy. The method was then applied to fit BRDF data for different common materials. Finally, NLOS target simulations with varying surface materials were reconstructed using the confocal diffusion tomography algorithm. The reconstructed NLOS images were classified via a convolutional neural network to assess how different surface materials impacted imaging quality. The results show that image clarity improves when decreasing the specular reflection and increasing the diffuse reflection, with the best results obtained for surfaces exhibiting a high diffuse reflection and no specular reflection.

Machine learning-based intelligent localization technique for channel classification in massive MIMO

Multiple-input multiple-output (MIMO) technology has been widely adopted in wireless communications, which enables the simultaneous transmission of multiple data streams via multiple transmitting and receiving antennas. In a MIMO system with non-line-of-sight (NLOS), transmitted signals are reflected by various obstacles along the path, reaching the antenna at different angles and times. In 5G networks, the NLOS problem is a major challenge for massive MIMO localization, significantly reducing positioning accuracy. In this work, an intelligent localization technique based on NLOS identification and mitigation is proposed to address this problem. In this solution, a Convolutional Neural Network (CNN) based hybrid Archimedes-based Salp Swarm Algorithm (HASSA) technique is proposed to detect NLOS or the line of sight (LOS) and estimate the location. The accuracy can be analyzed by considering the angle of arrival of signals, threshold-based time of arrival, and time difference of arrival from different antennas. A novel reinforcement learning-based optimization approach is used for the mitigation of NLOS in the radio wave propagation path, which in turn reduces the computational complexity. We use the Ensemble Deep Deterministic Policy Gradient-Based Approach (EDDPG)-based Honey Badger algorithm (HBA) for the aforementioned process. The simulation of this approach assesses diverse scenarios and considers different parameters, and the approach is compared with various state-of-the-art works. From the simulation results, our proposed approach can be used for the identification and detection of LOS and NLOS components and can precisely enhance the localization compared with other approaches.

Analysis of Machine Learning-Based NLOS Signal Identification Algorithm for UWB Indoor Localization Using CIR Waveform Features

Abstract. Ultra-wideband (UWB) technology stands out among numerous indoor positioning techniques due to its high operating frequency, low interception capability, resistance to multipath effects, and strong penetration. The UWB uses the time-of-arrival (TOA) to estimate the distance between the transmitter and receiver anchors in centimeter accuracy. However, in complex indoor positioning environments, obstacles such as walls, glass windows, metal plates, and wooden doors may block and reflect signals, inevitably causing non-line-of-sight (NLOS) errors that significantly affect positioning accuracy. The NLOS signal has lower signal energy due to the reflections. Thus, the channel impulse responses (CIR) from NLOS and LOS are different. To address the NLOS signal identification issue in UWB positioning, we utilize UWB CIR data collected from various positioning scenarios as the data source. CIR waveform input features are provided for the NLOS signal recognition model, and four machine learning models—Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), K-Nearest Neighbors (KNN), and XGBoost—are trained and optimized for NLOS signal recognition. The aim of the study is to analyze the performance of different machine learning algorithms for NLOS signal recognition in UWB indoor localization using these features. Experimental results indicate that machine learning-based NLOS signal recognition algorithms can achieve an accuracy of approximately 77.46%, precision of 80.46%, and an F1 score of 0.81. Among the four models, the XGBoost model demonstrates generally better recognition performance.

Simultaneous lightwave information and power transfer for NLOS ultraviolet communications under different weather conditions

In this work, a power allocation scheme for ultraviolet communication (UVC) with simultaneous lightwave information and power transfer (SLIPT) is proposed. Based on the non-line-of-sight (NLOS) single scattering channel model, the path loss and bit error rate (BER) performance of the UVC system with different geometrical parameters are analyzed. The effects of three types of weather, light rain, fine weather and severe fog conditions are considered. It is proved that the path loss is minimized on foggy days with minimum BER, followed by sunny days. The proposed SLIPT approach that the receiving energy is in turn divided into two parts, one for energy harvesting (EH) and the other for information decoding (ID). The system is designed to optimize energy harvesting for autonomous power supply, while ensuring a specified BER threshold and maintaining spectral efficiency. The effects of three types of weather on the SLIPT system are further investigated. It is demonstrated that under shorter communication distance(r), smaller elevation angle of receiving terminal (βR), larger field of view (FoV) and lower spectral efficiency(η) conditions, a higher percentage of energy is allocated for EH, with foggy days being the most favorable. The extreme geometric parameters of rainy condition can lead to the inability of communication, which needs to be limited to r ≤ 10 m, elevation angle of Rx ≤ 6°, FOV ≥100°, spectral efficiency ≤3, under the preset parameters in this paper.