Non-line-of-sight Research Articles

A recognition algorithm for NLOS-anchors in wireless location applications

In recent years, China's technological level has undergone rapid development, and technologies such as wireless local area networks and wireless sensor networks have also undergone rapid development in this situation. Under the influence of developed networks, many high-tech items are applied in our daily lives, such as intelligent transportation, remote control and monitoring, intelligent household appliances, and so on. In this environment, internet technology is rapidly developing and widely applied in various fields, making huge contributions to positioning technology and monitoring technology. The demand for high-precision, low-cost, strong applicability, and simple operation scientific technology in various industries is constantly increasing. Ultra wideband technology has played a significant role in the field of indoor positioning due to its unique advantages. However, in complex environments, the propagation of broadband signals is easily affected by obstacles, which weakens the signal received and seriously affects indoor positioning accuracy. As people gradually move into cities, the increase in population has caused increasingly serious traffic congestion. In order to solve the positioning problem of urban people, positioning industries that serve people have gradually emerged in the public's career, which has led to an increasing demand for accurate positioning. The complex environment of cities and the complex environment where people live can have a serious impact on positioning technology, leading to continuous deterioration of positioning accuracy. Non-Line of Sight (NLOS) is an important factor affecting positioning accuracy. So, this article conducts a thorough study on wireless local area networks and wireless sensor networks, fully improving the development of wireless positioning, and conducting a thorough study on the algorithm of NLOS- anchor.

Cohesive framework for non-line-of-sight imaging based on Dirac notation.

The non-line-of-sight (NLOS) imaging field encompasses both experimental and computational frameworks that focus on imaging elements that are out of the direct line-of-sight, for example, imaging elements that are around a corner. Current NLOS imaging methods offer a compromise between accuracy and reconstruction time as experimental setups have become more reliable, faster, and more accurate. However, all these imaging methods implement different assumptions and light transport models that are only valid under particular circumstances. This paper lays down the foundation for a cohesive theoretical framework which provides insights about the limitations and virtues of existing approaches in a rigorous mathematical manner. In particular, we adopt Dirac notation and concepts borrowed from quantum mechanics to define a set of simple equations that enable: i) the derivation of other NLOS imaging methods from such single equation (we provide examples of the three most used frameworks in NLOS imaging: back-propagation, phasor fields, and f-k migration); ii) the demonstration that the Rayleigh-Sommerfeld diffraction operator is the propagation operator for wave-based imaging methods; and iii) the demonstration that back-propagation and wave-based imaging formulations are equivalent since, as we show, propagation operators are unitary. We expect that our proposed framework will deepen our understanding of the NLOS field and expand its utility in practical cases by providing a cohesive intuition on how to image complex NLOS scenes independently of the underlying reconstruction method.

Accurate Localization in LOS/NLOS Channel Coexistence Scenarios Based on Heterogeneous Knowledge Graph Inference

Accurate localization is one of the basic requirements for smart cities and smart factories. In wireless cellular network localization, the straight-line propagation of electromagnetic waves between base stations and users is called line-of-sight (LOS) wireless propagation. In some cases, electromagnetic wave signals cannot propagate in a straight line due to obstruction by buildings or trees, and these scenarios are usually called non-LOS (NLOS) wireless propagation. Traditional localization algorithms such as TDOA, AOA, etc. , are based on LOS channels, which are no longer applicable in environments where NLOS propagation is dominant, and in most scenarios, the number of base stations with LOS channels containing users is often small, resulting in traditional localization algorithms being unable to satisfy the accuracy demand of high-precision localization. In addition, some nonideal factors may be included in the actual system, all of which can lead to localization accuracy degradation. Therefore, the approach developed in this paper uses knowledge graph and graph neural network (GNN) technology to model communication data as knowledge graphs, and it adopts the knowledge graph inference technique based on a heterogeneous graph attention mechanism to infer unknown data representations in complex scenarios based on the known data and the relationships between the data to achieve high-precision localization in scenarios with LOS/NLOS channel coexistence. We experimentally demonstrate a spatial 2D localization accuracy level of approximately 10 meters on multiple datasets and find that our proposed algorithm has higher accuracy and stronger robustness than the state-of-the-art algorithms.

Ultra-Wideband Ranging Error Mitigation with Novel Channel Impulse Response Feature Parameters and Two-Step Non-Line-of-Sight Identification.

The effective identification and mitigation of non-line-of-sight (NLOS) ranging errors are essential for achieving high-precision positioning and navigation with ultra-wideband (UWB) technology in harsh indoor environments. In this paper, an efficient UWB ranging-error mitigation strategy that uses novel channel impulse response parameters based on the results of a two-step NLOS identification, composed of a decision tree and feedforward neural network, is proposed to realize indoor locations. NLOS ranging errors are classified into three types, and corresponding mitigation strategies and recall mechanisms are developed, which are also extended to partial line-of-sight (LOS) errors. Extensive experiments involving three obstacles (humans, walls, and glass) and two sites show an average NLOS identification accuracy of 95.05%, with LOS/NLOS recall rates of 95.72%/94.15%. The mitigated LOS errors are reduced by 50.4%, while the average improvement in the accuracy of the three types of NLOS ranging errors is 61.8%, reaching up to 76.84%. Overall, this method achieves a reduction in LOS and NLOS ranging errors of 25.19% and 69.85%, respectively, resulting in a 54.46% enhancement in positioning accuracy. This performance surpasses that of state-of-the-art techniques, such as the convolutional neural network (CNN), long short-term memory-extended Kalman filter (LSTM-EKF), least-squares-support vector machine (LS-SVM), and k-nearest neighbor (K-NN) algorithms.

Multi-Subject 3D Human Mesh Construction Using Commodity WiFi

This paper introduces MultiMesh, a multi-subject 3D human mesh construction system based on commodity WiFi. Our system can reuse commodity WiFi devices in the environment and is capable of working in non-line-of-sight (NLoS) conditions compared with the traditional computer vision-based approach. Specifically, we leverage an L-shaped antenna array to generate the two-dimensional angle of arrival (2D AoA) of reflected signals for subject separation in the physical space. We further leverage the angle of departure and time of flight of the signal to enhance the resolvability for precise separation of close subjects. Then we exploit information from various signal dimensions to mitigate the interference of indirect reflections according to different signal propagation paths. Moreover, we employ the continuity of human movement in the spatial-temporal domain to track weak reflected signals of faraway subjects. Finally, we utilize a deep learning model to digitize 2D AoA images of each subject into the 3D human mesh. We conducted extensive experiments in real-world multi-subject scenarios under various environments to evaluate the performance of our system. For example, we conduct experiments with occlusion and perform human mesh construction for different distances between two subjects and different distances between subjects and WiFi devices. The results show that MultiMesh can accurately construct 3D human meshes for multiple users with an average vertex error of 4cm. The evaluations also demonstrate that our system could achieve comparable performance for unseen environments and people. Moreover, we also evaluate the accuracy of spatial information extraction and the performance of subject detection. These evaluations demonstrate the robustness and effectiveness of our system.

TDoA positioning with data-driven LoS inference in mmWave MIMO communications

Location awareness is an essential feature to support various mobile services, and cooperative positioning with channel state information (CSI) in millimeter wave multiple-input multiple-output (MIMO) networks is promising. Meanwhile, strong non-line of sight (NLoS) effects in outdoor scenarios severely reduce the model-based localization accuracy, and existing fingerprint-based methods have a critical requirement for labeled data and lack scalability. To overcome these shortcomings, we propose a hybrid data-and-model driven cooperative localization scheme using uplink wideband MIMO CSI at multiple base stations (BS) as measurements. First, we design a data-driven line of sight (LoS) inference module, realized by a transformer with an attention mechanism, to estimate the statistics of LoS arrival times. Each BS is equipped with a module, and the counterpart LoS inferences are simultaneously and distributedly performed. Second, we locate the mobile user by a model-driven approximate maximum likelihood time difference of arrival algorithm with the estimated statistics. Experiment results in urban scenarios show that the proposed localization scheme is extensible and also robust to varying number of channel paths and combination of BS measurements. Considering a small training dataset, the proposed scheme significantly outperforms the data- and model-driven baselines in NLoS scenarios, in terms of positioning accuracy.

Miniaturized time-correlated single-photon counting module for time-of-flight non-line-of-sight imaging applications.

Single-photon time-of-flight (TOF) non-line-of-sight (NLOS) imaging enables the high-resolution reconstruction of objects outside the field of view. The compactness of TOF NLOS imaging systems, entailing the miniaturization of key components within such systems, is crucial for practical applications. Here, we present a miniaturized four-channel time-correlated single-photon counting module dedicated to TOF NLOS imaging applications. The module achieves excellent performance with a 10ps bin size and 27.4ps minimum root-mean-square time resolution. We present the results of the TOF NLOS imaging experiment using an InGaAs/InP single-photon detector and the time-correlated single-photon counting module and show that a 6.3cm lateral resolution and 2.3cm depth resolution can be achieved under the conditions of 5m imaging distance and 1ms pixel dwell time.

A Compact, Low-Profile, Broadband Quasi-Isotropic Antenna for Non-Line-of-Sight Communications

A single-feed broadband quasi-isotropic antenna was designed for non-line-of-sight (NLOS) wireless sensor networks. The proposed antenna is based on a combination of fork-shaped crossed dipoles. It shows the broadband of quasi-isotropic radiation characteristics with high radiation efficiency. The electrical size ka of the proposed antenna is 0.94 with respect to its lower operating frequency. Its profile is also extremely thin at 0.0015λ. The impedance is matched from 1.8 to 4.3 GHz, or an 81.9% fractional bandwidth, whereas the maximum gain deviation ranging from 6.2 to 9.2 dB for the quasi-isotropic radiation is achieved from 1.8 to 3.6 GHz with a 10 dB criterion, which is close to the impedance bandwidth. The performance from the computed expectations is verified, as it shows a gain deviation of 8.4–9.8 dB from 1.9 to 3.3 GHz with an 80% fractional impedance bandwidth. The proposed antenna also shows good spatial coverage of circular polarization at high frequencies. Lastly, the received power level performance of the proposed antenna is tested under the NLOS condition, which shows a higher level compared to the linearly polarized, broadband omni-directional monopole antenna.

A Low-Profile Circularly Polarized Millimeter-Wave Broadband Antenna Analyzed with a Link Budget for IoT Applications in an Indoor Scenario.

Broadband antennas with a low-profile generating circular polarization are always in demand for handheld/ portable devices as CP antennas counter multipath and misalignment issues. Therefore, a compact millimeter-wave antenna is proposed in this article. The proposed antenna structure comprises two circular rings and a circular patch at the center. This structure is further embedded with four equilateral triangles at a 90° orientation. The current entering the radiator is divided into left and right circular directions. The equilateral triangles provide the return path for current at the differential phase of ±90°, generating circular polarization. Structural development and analysis were initially performed through the characteristic mode theory. It showed that Modes 1 to 4 generated good impedance matching from 20 to 30 GHz and Modes 1 to 5, from 30 to 40 GHz. It also demonstrated the summation of orthogonal modes leading to circular polarization. The antenna-measured reflection coefficient |S11| > 10 dB was 19 GHz (23-42 GHz), and the axial ratio at -3 dB was 4.2 GHz (36-40.2 GHz). The antenna gain ranged from 4 to 6.2 dBi. The proposed antenna was tested for link margin estimation for IoT indoor conditions with line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. The communication reliability with co- and cross-polarization was also studied under these conditions, and the results proved to be satisfactory.

Novel Intelligent Methods for Channel Path Classification and Model Determination Based on Blind Source Signals

In this paper, the urban signal propagation characteristics based on the location of blind sources are investigated. To address the issue of blind electromagnetic radiation sources in complex urban environments, intelligent methods for propagation channel path classification, and model determination are brought forth based on field test data. The intelligent classification method distinguishes between the Line-of-Sight (LoS) path channel and a direct path, the LoS multipath channel with a direct path and other multiple paths, and the Non-Line-of-Sight (NLoS) multipath channel without a direct path from the source to the test point. The modeling aspect determines the model type to which the received signal belongs based on the statistical model derived from the tested data of a specific source. A validation measurement system was constructed for the FM broadcasting band, and validation campaigns were conducted in the city of Wuhan. The process and analysis of the data using this method demonstrate the accurate distinction of the different propagation path channels and models and involve the construction of a statistical model for the FM band in Wuhan’s urban area.

Theoretical and experimental studies on channel impulse response of short-range non-line-of-sight ultraviolet communications.

A theoretical channel impulse response (CIR) model of short-range non-line-of-sight (NLOS) ultraviolet communications (UVC) in noncoplanar geometry under the single-scatter condition is proposed. Simulation results obtained from the widely accepted Monte-Carlo (MC)-based channel model of NLOS UVC are provided to verify corresponding theoretical results obtained from the proposed theoretical single-scatter CIR model. Additionally, an outdoor experiment with a light-emitting diode (LED) as the light source is first designed to measure the channel step response of NLOS UVC and to further validate the proposed theoretical single-scatter CIR model. By varying the different parameters of the transmitter and the receiver, such as the baseline range, the inclination angle, the azimuth angle, the beam divergence angle, and the field-of-view angle, the results of the proposed theoretical single-scatter CIR model and the MC-based channel model are exhibited and further analyzed in detail. Results indicate that the computational time cost by the proposed theoretical single-scatter CIR model is decreased to less than 0.6% of the MC-based one with comparable accuracy in assessing the temporal characteristics of NLOS UVC channels. Additionally, theoretical results obtained from the proposed theoretical single-scatter CIR model manifest satisfactory agreement with corresponding experimental measurements.

Towards a more accurate light transport model for non-line-of-sight imaging.

Non-line-of-sight (NLOS) imaging systems involve the measurement of an optical signal at a diffuse surface. A forward model encodes the physics of these measurements mathematically and can be inverted to generate a reconstruction of the hidden scene. Some existing NLOS imaging techniques rely on illuminating the diffuse surface and measuring the photon time of flight (ToF) of multi-bounce light paths. Alternatively, some methods depend on measuring high-frequency variations caused by shadows cast by occluders in the hidden scene. While forward models for ToF-NLOS and Shadow-NLOS have been developed separately, there has been limited work on unifying these two imaging modalities. Dove et al introduced a unified mathematical framework capable of modeling both imaging techniques [Opt. Express27, 18016 (2019)10.1364/OE.27.018016]. The authors utilize this general forward model, known as the two frequency spatial Wigner distribution (TFSWD), to discuss the implications of reconstruction resolution for combining the two modalities but only when the occluder geometry is known a priori. In this work, we develop a graphical representation of the TFSWD forward model and apply it to novel experimental setups with potential applications in NLOS imaging. Furthermore, we use this unified framework to explore the potential of combining these two imaging modalities in situations where the occluder geometry is not known in advance.

26‐GHz Testbed performance evaluation using system EVM characterization for 5G communication

SummaryThe millimeter‐wave (mmW) band has been chosen to obtain multi‐gigabit per second in the fifth generation (5G) communication and beyond. The knowledge of this frequency band is still the focus of research today. After UTI announced its choice of mmW for 5G communication, massive studies were carried out on this band. Among these studies, many optimization methods were proposed to reduce the impact of degradation sources. In this paper, we propose a new method to optimize the system error vector magnitude (EVM) of complex systems with many different calibration sources. A difference of 0.1% is observed between our optimization method and old methods. This small improvement of the EVM system can push the 3GPP system EVM (EVM at the receiver) limits by a few centimeters, which is not negligible in a high‐frequency transmission. Moreover, we have also proposed a new method to compute the throughput that takes into account the TX and RX directional antenna heights. The measurement campaigns and simulation results have shown an improvement from 0% to 6% of throughput. The results depend on indoor environment study and different obstacles in NLOS (non‐line of sight) conditions.

Cooperative navigation of unmanned aerial vehicle formation with delayed measurement

Abstract This paper focused on the problem of positioning accuracy degradation caused by delayed measurement information in Unmanned Aerial Vehicle (UAV) formation cooperative navigation under complex environments such as cities and hills, and presented a non-synchronous compensation algorithm based on kinematic constraints and constructed a distributed cooperative navigation filter based on the analysis of the basic operating characteristics of inertial devices, satellite receivers, and ranging sensors. In the UAV formation, the leader-UAV is equipped with Real-Time Kinematic (RTK) differential equipment and airborne data link to construct the airborne reference beacons and provide cooperative navigation services for the wingmen-UAV. Firstly, the navigation filtering framework with inertial sensors as the core is established. Secondly, the non-synchronous compensation filter is constructed by using the kinematic constraint model, which compensates and corrects the non-synchronous air-based position of the leader-UAV, and reduces the effect of delayed measurement on the positioning error of the system. Then the fault diagnosis algorithm is utilized to complete the identification and rejection of abnormal range values in the case of Non-Line-of-Sight (NOLS). Finally, the navigation parameters are solved by the Kalman filter. Simulation results show that the non-synchronous compensated filtering proposed in this paper can improve the absolute positioning accuracy by 55%, which effectively improves the cooperative navigation performance and robustness under the presence of random time delay in the measurement information.

Resilient Multi-Sensor UAV Navigation with a Hybrid Federated Fusion Architecture

Future UAV (unmanned aerial vehicle) operations in urban environments demand a PNT (position, navigation, and timing) solution that is both robust and resilient. While a GNSS (global navigation satellite system) can provide an accurate position under open-sky assumptions, the complexity of urban operations leads to NLOS (non-line-of-sight) and multipath effects, which in turn impact the accuracy of the PNT data. A key research question within the research community pertains to determining the appropriate hybrid fusion architecture that can ensure the resilience and continuity of UAV operations in urban environments, minimizing significant degradations of PNT data. In this context, we present a novel federated fusion architecture that integrates data from the GNSS, the IMU (inertial measurement unit), a monocular camera, and a barometer to cope with the GNSS multipath and positioning performance degradation. Within the federated fusion architecture, local filters are implemented using EKFs (extended Kalman filters), while a master filter is used in the form of a GRU (gated recurrent unit) block. Data collection is performed by setting up a virtual environment in AirSim for the visual odometry aid and barometer data, while Spirent GSS7000 hardware is used to collect the GNSS and IMU data. The hybrid fusion architecture is compared to a classic federated architecture (formed only by EKFs) and tested under different light and weather conditions to assess its resilience, including multipath and GNSS outages. The proposed solution demonstrates improved resilience and robustness in a range of degraded conditions while maintaining a good level of positioning performance with a 95th percentile error of 0.54 m for the square scenario and 1.72 m for the survey scenario.

Lab to Multiscene Generalization for Non-Line-of-Sight Identification With Small-Scale Datasets

Ultra-wideband (UWB) wireless indoor positioning systems rely on time of flight (TOF) to estimate distances but can be biased and miscalculated due to non-line-of-sight (NLOS) transmission channels in complex environments. Therefore, to remove errors, several machine learning techniques have been proposed for identifying NLOS signals from Channel Impulse Responses (CIRs). However, as CIR signals could be heavily influenced by various environments, current NLOS classifiers are not universal to provide satisfactory accuracy for new scenarios and require detailed measurements on a large number of CIRs for training. Hence, we propose a generalization method based on data augmentation via noise injection and transfer learning to allow the deep neural network (DNN) trained under a lab condition to be applied to various and even harsh practical scenarios with the need to measure massive training data minimized. This paper presents the first demonstration that it is effective to utilize a lab-based pre-trained DNN for real-world transfer and white Gaussian noise data augmentation for ML-based NLOS identification on UWB CIRs to address the problem when it is not feasible to measure sufficient training data. Our testing results show that in two scenarios, corridor and parking lot, with only 50 CIR signals as the training set, the accuracy of the NLOS identification model after applying the proposed method is increased from 84.4% to 98.8% and from 81.1% to 97.1%, respectively.

A Non-Stationary Channel Model for 6G Multi-UAV Cooperative Communication

This paper proposes a novel three-dimensional (3D) non-stationary multiple-unmanned aerial vehicle (multi-UAV) cooperative channel model for sixth generation (6G) wireless communication systems. In the proposed multi-UAV cooperative channel model, the channel impulse response (CIR) of sub-channel between each UAV and ground station (GS) is calculated, including line-of-sight (LoS), non-LoS (NLoS), as well as ground reflection transmissions. The relative distance and transmission relationship among sub-channels between multi-UAVs and GS are further modeled and analyzed. To simultaneously capture the cooperative non-stationarity of multi-UAV cooperative channels in space and time domains, a novel cooperation-based space-time (S-T) non-stationary algorithm is developed based on the birth-death (BD) process for the first time. Meanwhile, the UAV-related parameters, e.g., the height and 3D movement of UAVs, are considered. Some important channel statistical properties, such as space-time-frequency correlation function (STF-CF), Doppler power spectral density (DPSD), cooperative time stationary interval, and singular value spread (SVS), are obtained. Based on the simulation results, multi-UAV cooperative channel statistical properties are analyzed. Finally, the simulation results match well with the ray-tracing results, which validates the accuracy of the proposed multi-UAV channel model.

Investigation of non-line-of-sight underwater optical wireless communications with wavy surface.

Underwater optical wireless communication (UOWC) systems have been widely researched to achieve high-speed and secure wireless communications. The non-line-of-sight (NLOS) UOWC system that uses the water surface to reflect signal light is widely studied to overcome the line-of-sight (LOS) channel limitation, particularly the channel blockage issue by marine biology or complex underwater topography. However, most previous NLOS UOWC studies have assumed a flat water surface or a general sine or cosine surface wave model for simplicity, leading to inaccurate performance estimations. In this paper, we build a theoretical NLOS UOWC framework with the Pierson wave model which considers both spatial correlation and time relativity information of wave incorporating wind speed, and investigate the signal-noise-ratio (SNR) and bit-error-rate (BER) performance. Results show that compared with the previous flat surface, the wavy surface can reduce the probability of achieving a satisfying signal level by up to 70%, affecting the performance of NLOS UOWC systems. Furthermore, we investigate the multiple-input-multiple-output (MIMO)-based NLOS UOWC under wavy surfaces. Results show that the MIMO principle can reduce the impact of the wavy surface, where the probability of achieving a satisfying signal level can be increased by up to 50% using the 2 × 4 MIMO configuration. However, results also show that further increasing the number of receivers may not further improve the system performance. The proposed model enables more accurate design and analysis of NLOS UOWC systems by accounting for the overlooked impact of wavy surfaces.

Bit error rate analysis of MRC diversity receiver over shadowed Beaulieu‐Xie fading channels for mmWave communications

SummaryThis paper presents the performance of maximum ratio combining (MRC) diversity receiver over shadowed Beaulieu‐Xie (SBX) fading model. The SBX model is used to characterise wireless communication environments that include multiple numbers of line‐of‐sight (LOS) and non‐line‐of‐sight (NLOS) signals. The probability density function (PDF) of the MRC scheme over the SBX channel has been derived. The PDF‐based approach has been used to calculate the outage performance and average bit error rate of several modulation schemes for coherent and non‐coherent signals. It has also been shown how the diversity orders and the fading channel's parameters affect the system's performance. To authenticate the evaluated expressions, Monte‐Carlo simulation results are used for validation. The SBX model is used to characterise wireless communication environments that include multiple numbers of LOS and NLOS signals.

Evaluation of ML-based classification algorithms for GNSS signals in ocean environment

Abstract In the maritime environment, multipath interference exhibits a significantly pronounced influence, resulting in GNSS system performance degradation. Enhancing system performance involves the identification and elimination of NLOS signals. This study focuses on the analysis of multipath data induced by sea waves, collected off the coast of Kakinada Sea (16.98° N, 82.29° E), to categorize signals as Line-of-Sight (LOS), Non-Line-of-Sight (NLOS) and Multipath (MP). A machine learning (ML) approach is employed to identify the presence of LOS, NLOS and MP signals in a coastal environment, both before and after the advancement of tidal waves. In the proposed approach, ML algorithms are trained using 3 key parameters namely elevation angle, signal strength and pseudorange residuals. This study involves the implementation of 14 prominent supervised classification algorithms and their accuracies and computational times are compared. The results due to GPS (L1) and IRNSS (L5 and S1) are considered. Decision Tree and its ensemble function AdaBoost, exhibited exceptional performance of accuracy (99.99 %) and computational time (0.45 s).