Multipath Effects Research Articles

An Improved Velocity-Aided Method for Smartphone Single-Frequency Code Positioning in Real-World Driving Scenarios

The availability of Global Navigation Satellite System (GNSS) raw observations in smartphones has driven research into low-cost GNSS solutions, especially in challenging urban environments, which have garnered significant attention from scholars in recent years. This study proposes an improved smartphone-based velocity-aided positioning method and conducts vehicle-mounted experiments in urban roads representing typical scenarios. The results show that when transitioning from low- to high-multipath environments, the number of visible satellites and carrier phase observations are highly sensitive to environmental factors, with frequent multipath effects. The introduction of robust pre-fit and post-fit residual algorithms has proven to be an effective quality control method. Additionally, using more refined observation models and appropriate parameter estimation algorithms led to a slight 6% improvement in velocity performance. The improved Kalman filter position estimation model (KFSPP-P) strategy, by incorporating velocity uncertainty into the state estimation process, overcomes the limitations of conventional velocity-aided smartphone positioning methods (KFSPP-V) in complex urban environments. In low-multipath environments, the accuracy of the KFSPP-P strategy is comparable to that of KFSPP-V, with an approximate 8% improvement in horizontal accuracy. However, in more challenging environments, such as tree-lined roads and urban environments, the KFSPP-P strategy shows significant improvements, particularly enhancing horizontal positioning accuracy by approximately 50%. These advancements demonstrate the potential of using smartphones to provide reliable positioning services in complex urban environments.

Reduction of Multipath Effect in GNSS Positioning by Applying Pseudorange Acceleration as Weight

A novel approach is proposed to mitigate the multipath effect, considered a major source of error in global navigation satellite system (GNSS) positioning in urban areas. We utilize code pseudorange acceleration measurements as a weight in a least squares estimation process. If GNSS signals are reflected off a surrounding surface, they cause large variations in the recorded pseudorange measurement. Accelerations computed at each epoch with three consecutive pseudoranges exhibit significant fluctuations in a multipath signal. As a result, positioning accuracy improved by 75% horizontally and 79% vertically compared to not applying any weight. Even when multipath errors exist, the range acceleration (RA) value is sometimes low at many epochs. When a threshold value for the signal-to-noise ratio was additionally applied besides RA, the positioning accuracy at two test sites (including a deep urban environment) improved by more than 80% in both horizontal and vertical directions.

Cross-Layer Routing Protocol Based on Channel Quality for Underwater Acoustic Communication Networks

Due to the physical characteristics of acoustic channels, the performance of underwater acoustic communication networks (UACNs) is more susceptible to the impacts of multipath and Doppler effects. Channel quality can serve as a measure of the reliability of underwater communication links. A cross-layer routing protocol based on channel quality (CLCQ) is proposed to improve the overall network performance and resource utilization. First, the BELLHOP ray model is used to calculate the channel impulse response combined with the winter sound speed profile data of a specific sea area. Then, the channel impulse response is integrated into the communication system to evaluate the channel quality between nodes based on the bit error rate (BER). Finally, during the selection of the next hop node, a reinforcement learning algorithm is employed to facilitate cross-layer interaction within the protocol stack. The optimal relay node is determined by the channel quality index (BER) from the physical layer, the buffer state from the data link layer, and the node residual energy. To enhance the algorithm’s convergence speed, a forwarding candidate set selection method is proposed which takes into account node depth, residual energy, and buffer state. Simulation results show that the packet delivery rate (PDR) of the CLCQ is significantly higher than that of Q-Learning-Based Energy-Efficient and Lifetime-Extended Adaptive Routing (QELAR) and Geographic and Opportunistic Routing (GEDAR).

A graphics-based digital twin (GBDT) framework for accurate UAV localization in GPS-denied environments

Autonomous navigation of Unmanned Aerial Vehicles (UAVs) is crucial for effective assessment of large-scale civil infrastructure, as manual UAV control is time consuming and prone to mishaps. Autonomous navigation outdoors typically employs GPS signals to enable accurate localization and reduce long-term drifts. However, in many civil engineering applications, GPS signals are either poor or unavailable due to interference and/or multi-path effects. Current approaches for UAV localization in GPS-denied environments, track geometric features such as corners; however, these natural features can be misrepresented due to the presence of occlusions and/or image processing errors, resulting in significant localization errors that can grow with time. AprilTags can reduce localization errors, but installation on large-scale civil infrastructure can be challenging. Therefore, this paper proposes a framework that leverages the wealth of visual and geometric information encoded in a Graphics-Based Digital Twin (GBDT) of a target infrastructure to provide accurate localization of a UAV. The GBDT is comprised of a computer graphics model that faithfully represents a target structure’s geometry, structural features, and visual textures. The visual details of the GBDT are exploited to design an object recognition algorithm that detects GBDTtags, which are distinctive objects (or a collection of components) on the target structure in advance; GBDTtags provide functionality similar to AprilTags. When the camera attached to a UAV detects one or more GBDTtags, the vertices of the GBDTtags are mapped from the image plane into the GBDT coordinate system, allowing for the UAV to be localized. The framework, termed herein as GBDTpose, is first validated numerically using Blender, Gazebo, and Mavros software-in-the-loop (SITL). Subsequently, field validation is carried out using the Kavita and Lalit Bahl Smart Bridge at the University of Illinois Urbana-Champaign (UIUC). Results show that localization in GPS-denied environments can be achieved with 5-50 cm accuracy without the need for physical markers being placed on the structure.

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.

Proposal of UAV-SLAM-Based 3D Point Cloud Map Generation Method for Orchards Measurements

This paper proposes a method for generating highly accurate point cloud maps of orchards using an unmanned aerial vehicle (UAV) equipped with light detection and ranging (LiDAR). The point cloud captured by the UAV-LiDAR was converted to a geographic coordinate system using a global navigation satellite system / inertial measurement unit (GNSS/IMU). The converted point cloud is then aligned with the simultaneous localization and mapping (SLAM) technique. As a result, a 3D model of an orchard is generated in a low-cost and easy-to-use manner for pesticide application with precision. The method of direct point cloud alignment with real-time kinematic-global navigation satellite system (RTK-GNSS) had a root mean square error (RMSE) of 42 cm between the predicted and true crop height values, primarily due to the effects of GNSS multipath and vibration of automated vehicles. Contrastingly, our method demonstrated better results, with RMSE of 5.43 cm and 2.14 cm in the vertical and horizontal axes, respectively. The proposed method for predicting crop location successfully achieved the required accuracy of less than 1 m with errors not exceeding 30 cm in the geographic coordinate system.

Modulation Classification of Underwater Communication Signals Based on Channel Estimation

Classifying modulated signals for non-cooperative underwater acoustic communication is challenging due to signal distortion caused by fading and multipath effects in the underwater acoustic channel. Our proposed method utilizes channel estimation parameters to measure and correct signal distortion, thereby enhancing the recognition performance of the received signal. Modulation classification experiments were conducted on a public dataset with various modulation schemes, as well as on the same dataset with simulated underwater acoustic channels. The results indicate that our method effectively mitigates the impact of the underwater acoustic channel on modulation signal classification, improves recognition accuracy, and is broadly applicable to a wide range of machine learning classifiers. Finally, we validated these findings using real underwater communication data.

A precise registration method for large-scale urban point clouds based on phased and spatial geometric features

Abstract The dense high-rise buildings and multipath effects in urban areas significantly reduce the positioning signal accuracy of laser scanning systems, leading to layering and offset issues in the collected point cloud data on the same road. In order to acquire comprehensive and consistent three-dimensional information on the objects, thereby providing field inspection data for large-scale road traffic network scenarios, in this paper, an improved point cloud registration method is proposed to divide the registration process into two stages: elevation registration and plane registration. Elevation registration takes the ground point cloud as the registration primitive, reduces the number of point clouds through curvature down-sampling, and constrains the feature point sequence with a fixed range to provide a good initial pose for fine registration. The plane registration first inherits the elevation registration parameters, combining the dynamic distance parameters of spherical region step based on the median, using robust multi-scale loss functions to address residual points, effective adjacent point pairs are selected to obtain the spatial transformation matrix, and realizes accurate registration. Experimental results with multiple sets of urban point cloud data show that the root mean square error of point cloud registration can be controlled within 0.06 m, achieving a relatively superior registration accuracy, it can provide detailed prior data for measurement information analysis.

UWB-Assisted Bluetooth Localization Using Regression Models and Multi-Scan Processing.

Bluetooth devices have been widely used for pedestrian positioning and navigation in complex indoor scenes. Bluetooth beacons are scattered throughout the entire indoor walkable area containing stairwells, and pedestrian positioning can be obtained by the received Bluetooth packets. However, the positioning performance is sharply deteriorated by the multipath effects originating from indoor clutter and walls. In this work, an ultra-wideband (UWB)-assisted Bluetooth acquisition of signal strength value method is proposed for the construction of a Bluetooth fingerprint library, and a multi-frame fusion particle filtering approach is proposed for indoor pedestrian localization for online matching. First, a polynomial regression model is developed to fit the relationship between signal strength and location. Then, particle filtering is utilized to continuously update the hypothetical location and combine the data from multiple frames before and after to attenuate the interference generated by the multipath. Finally, the position corresponding to the maximum likelihood probability of the multi-frame signal is used to obtain a more accurate position estimation with an average error as low as 70 cm.

Modeling and analysis of propagation characteristics of WBAN in hospital ward scenarios

AbstractTo explore the feasibility of high‐frequency indoor wireless body area network (WBAN) communications, the propagation characteristics of WBAN in simulated hospital ward scenarios at 11 GHz are studied. Based on the extensive measured data, the impact of the patient's lying angle (LA) on channel characteristics has been analyzed, such as statistical characteristics of path loss (PL), shadowing effect, and root mean square delay spread. Moreover, a PL model with LA factor is presented for the hospital ward scene, and the influence of LA on PL in the presence of height difference is also analyzed. The research results indicate that, the PL caused by LA (abbreviated as PLA) follows a power function with respect to the LA in case of a fixed distance between the transmitter and receiver. When the LA is fixed, PLA exhibits a cubic function relationship with the distance between the transmitter and receiver. In addition, the presence of patient's LA can cause more significant multipath effect. However, the impact of LA gradually weakens as the height difference increases when there is a height difference. The research results provide a theoretical basis and practical basis for utilizing the 11 GHz frequency band in indoor WBAN in the future.

Markov chain-based temporal correlation processing algorithm in reverberant environment by sparse Bayesian learning for acoustic imaging

Identifying sound sources is an important step in noise control, different sound source localization algorithms have been proposed and studied for almost a century. Most of the studies assume an ideal measurement environment, i.e., an anechoic or half-anechoic environment, but there are limited studies discussing localizing sound sources in a reverberation environment. This paper proposes an acoustic imaging method in a reverberation environment based on the sparse Bayesian learning framework. Initially, a sound propagation model was established by accounting for the multipath effects of the sound source in a reverberant environment. Based on the temporal correlation of a Markov chain, the algorithm is further improved in the sparse Bayesian learning framework. By leveraging the temporal correlation the algorithm provides, identifying the true source in a reverberant environment becomes more accurate. Numerical simulation results have shown that the proposed algorithm achieves real sound source localization and eliminates the influence of image sources in contour maps under realistic reverberant environments.

Research on the Fusion Positioning System of UWB/LiDAR Based on the Algorithm of SPF and KF

In order to overcome the disadvantages of single sensor localization in complex indoor environments, this paper proposes a design scheme for the ultra-wide band (UWB) and LiDAR co-location fusion system. To attain the ideal positioning effect of this system, a segmented point fusion approach based on Euclid's theorem is put forward to optimize the conventional extended Kalman filter and unscented Kalman filter algorithms. It can effectively reduce the positioning error of UWB in non-line-of-sight environments and under the multipath effect, and also assist LiDAR in correcting trajectory drift in sparsely textured scenes. Through both simulations and experiments, the feasibility of the proposed algorithm in the UWB/LiDAR positioning system is verified. The results reveal that the positioning error of the enhanced multi-sensor fusion algorithm is reduced by 22% compared with the original algorithm and 25.7% compared with the single sensor positioning method.

On the possibility of long‐distance transmission of OAM beam in rectangular tunnels

AbstractThis letter explores the possibility of long‐distance transmission of an orbital angular momentum (OAM) beam through rectangular tunnels. Theoretical analysis reveals that the OAM beam propagating in the tunnel with square cross section fulfills the conditions for axial propagation and exhibits azimuth symmetry. In comparison with free‐space OAM generation, we draw the conclusion that long‐distance transmission of OAM beams is achievable in the tunnels with square cross section. The validity of our conclusion is further substantiated through numerical experiments. Simulation results demonstrate that the first‐order OAM beam radiated by a uniform circular antenna array exhibits favorable helical phase distribution and the circular symmetry of the amplitude distribution in the tunnel beyond the Rayleigh distance. However, these characteristics degrade with increasing mode order and propagation distance, because the increase of high‐order OAM beam divergence angle and propagation distance will exacerbate the impact of multipath effects in the tunnel environment.

TWPT: Through-Wall Position Detection and Tracking System Using IR-UWB Radar Utilizing Kalman Filter-Based Clutter Reduction and CLEAN Algorithm

Against the backdrop of rapidly advancing Artificial Intelligence of Things (AIOT) and sensing technologies, there is a growing demand for indoor location-based services (LBSs). This paper proposes a through-the-wall localization and tracking (TWPT) system based on an improved ultra-wideband (IR-UWB) radar to achieve more accurate localization of indoor moving targets. The TWPT system overcomes the limitations of traditional localization methods, such as multipath effects and environmental interference, using the high penetration and high accuracy of IR-UWB radar based on multi-sensor fusion technology. In the study, an improved Kalman filter (KF) algorithm is used for clutter reduction, while the CLEAN algorithm, combined with a compensation mechanism, is utilized to increase the target detection probability. Finally, a three-point localization estimation algorithm based on multi-IR-UWB radar is applied for the precise position and trajectory estimation of the target. Experimental validation indicates the TWPT system achieves a high positioning accuracy of 96.91%, with a root mean square error (RMSE) of 0.082 m and a Maximum Position Error (MPE) of 0.259 m. This study provides a highly accurate and precise solution for indoor TWPT based on IR-UWB radar.

Chip-scale atomic clock (CSAC) aided GNSS in urban canyons

In urban canyons, the reflections and obstructions of Global Navigation Satellite System (GNSS) signals frequently lead to significant errors in measurements, the number of which can be larger than that of the correct measurements. This leads to a severe degradation of GNSS performance in urban canyons. Various fault detection and exclusion (FDE) algorithms have been developed to cope with these outliers caused by multipath effects. Most of these FDE algorithms check the consistency among measurements. However, in urban canyons, their effectiveness is significantly compromised by the lack of fault-free measurements. There is an urgent need to develop new constraints for enhancing GNSS FDE performance. In recent years, the advent of Chip-Scale Atomic Clock (CSAC), known for their affordability and high frequency stability, offers a promising solution for accurately predicting receiver clock errors. Additionally, using city maps to establish height constraints is another way to increase redundancy. The purpose of this study is to improve the GNSS positioning accuracy in urban canyons with the aid of CSAC and city map data. A novel FDE algorithm is developed to search for positions through the constraints of height and receiver clock. Extensive tests were conducted in urban canyons to evaluate the performance of the system. Results showed that the positioning accuracy can be improved from tens of meters to less than 6 m.

Research on an Autonomous Localization Method for Trains Based on Pulse Observation in a Tunnel Environment.

China's rail transit system is developing rapidly, but achieving seamless high-precision localization of trains throughout the entire route in closed environments such as tunnels and culverts still faces significant challenges. Traditional localization technologies cannot meet current demands, and the present paper proposes an autonomous localization method for trains based on pulse observation in a tunnel environment. First, the Letts criterion is used to eliminate abnormal gyro data, the CEEMDAN method is employed for signal decomposition, and the decomposed signals are classified using the continuous mean square error and norm method. Noise reduction is performed using forward linear filtering and dynamic threshold filtering, respectively, maximizing the retention of its effective signal components. A SINS/OD integrated localization model is established, and an observation equation is constructed based on velocity matching, resulting in an 18-dimensional complex state space model. Finally, the EM algorithm is used to address Non-Line-Of-Sight and multipath effect errors. The optimized model is then applied in the Kalman filter to better adapt to the system's observation conditions. By dynamically adjusting the noise covariance, the localization system can continue to maintain continuous high-precision position information output in a tunnel environment.

Perspectives for Improvements on Real-Time GNSS Positioning with the Use of New Observables

The research aims to analyze Global Navigation Satellite System (GNSS) positioning solutions using the Precise Point Positioning (PPP) method in kinematic mode, in which the position is obtained epoch by epoch through data from only one GNSS receiver, precise ephemerides, and satellite clock corrections, both with high accuracy. In this context, the adopted methodology employs the open-source software RTKLIB, which, through its data post-processing capability, enables the evaluation of kinematic PPP performance by incorporating new observables such as L5 and E5a bands, crucial for enhancing positional accuracy and mitigating multipath effects. Additionally, for survey simulations, data from the UFPR and POVE stations of the Brazilian Network for Continuous Monitoring of GNSS (RBMC) were utilized. The selected stations are located in the Northern and Southern regions of Brazil. The GNSS data were processed with the same tracking duration for a 45-minute cold-start, aimed at observing solution convergence. Subsequently, the estimated outputs in RTKLIB were obtained based on station origins using coordinates provided by SIRGAS-CON. Moreover, by utilizing precise ephemerides data, it was possible to conclude that the addition of new observables for triple-frequency positioning led to an improvement in the accuracy of the obtained coordinates, particularly in the Vertical component. In this regard, the increase in accuracy in experiments using only triple-frequency data with a 15° elevation mask was approximately 25% at the UFPR station and about ~37% at the POVE station. Furthermore, it was observed that reducing the elevation mask from 15° to 10° had a positive impact on dual-frequency positioning at the UFPR station, resulting in gains of over 3% in the East component and around ~21% in the North component compared to values obtained with the 15° mask. Similarly, during triple-frequency positioning at the POVE station, gains exceedingly approximately 16% in the North component and around ~22% in the Vertical component were observed.

Predicting Signal Reception Information from GNSS Satellites in Indoor Environments without Site Survey: Towards Opportunistic Indoor Positioning Based on GNSS Fingerprinting

While traditional GPS localization is impractical in certain indoor environments, GPS satellites offer a valuable source of indoor contextual information, particularly around window-side areas where signals are more accessible. This study introduces a new method that predicts GPS signal reception information in a target indoor environment without the need for actual data collection in the target environment. By predicting the strengths of GPS signals that are expected to be measured at various indoor positions, we can construct a GPS signal reception map of a target environment, which functions similarly to a Wi-Fi fingerprinting map surrounding window-side areas, offering a new dimension in indoor positioning systems. For instance, it aids in the correction of accumulated errors in pedestrian dead reckoning (PDR) systems. Our proposed method leverages easily accessible inputs, including satellite location data, indoor floorplan image of the target floor, and 3D mapping of the surrounding buildings, to predict signal reception at each position in the target environment. We developed a specialized neural network, named multi-scale branch fusion network (MSBF-Net), designed to process and integrate data of varying scales, such as the floorplan image of the target environment and 3D map of the surrounding area obtained from Google Earth, with a specific focus on understanding the line-of-sight (LOS) and multi-path effects caused by internal obstacles and surrounding buildings. This advanced capability enables the network to effectively interpret complex signal interactions within urban environments, enhancing its predictive accuracy for GPS signal reception. The effectiveness of our method was rigorously evaluated using real-world environments. In addition, we employed our method to implement opportunistic GPS fingerprint-based indoor positioning, where position estimates are provided when a strong signal is observed from at least one satellite. Surprisingly, the positioning method achieved a positioning error of only 2.8 meters when a smartphone is close to window-side areas even though the method does not rely on labeled training data collected in a target environment and signal infrastructures installed in the target environment.

GNSS/IMU/map-matching feedback integration with adaptive GNSS accuracy estimation by using low-quality sensors for vehicle localization in urban canyon

In urban canyons, the performance of vehicle navigation systems based on Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) is notably compromised due to satellite signal obstructions and severe multipath effects. This article proposes a GNSS/IMU/Map-Matching feedback (MMF) integration with the adaptive GNSS accuracy estimation to provide accurate and robust positions for vehicles in urban canyons. This system integrates self-constraints and MMF to control the drift of low- quality IMU. To avoid fatal positioning errors caused by incorrect MMF, motion states are categorized into straight-line driving and corner turning, with different criteria set based on their characteristics to validate MMF. Furthermore, GNSS accuracy is estimated adaptively with MMF. Extensive experiments in Hong Kong’s dense urban canyons demonstrated significantly improved positioning accuracy of 37% and 51% in the north and east directions, respectively, outperforming traditional GNSS/IMU/Map configurations. The results demonstrate the proposed approach’s marked enhancement in urban vehicular navigation.

Integration of attention mechanism and CNN-BiGRU for TDOA/FDOA collaborative mobile underwater multi-scene localization algorithm

The aim of this study is to address the issue of TDOA/FDOA measurement accuracy in complex underwater environments, which is affected by multipath effects and variations in water sound velocity induced by the challenging nature of the underwater environment. To this end, a novel cooperative localisation algorithm has been developed, integrating the attention mechanism and convolutional neural network-bidirectional gated recurrent unit (CNN-BiGRU) with TDOA/FDOA and two-step weighted least squares (ImTSWLS). This algorithm is designed to enhance the accuracy of TDOA/FDOA measurements in complex underwater environments. The algorithm initially makes use of the considerable capacity of a convolutional neural network (CNN) to extract profound spatial and frequency domain characteristics from multimodal data. These features are of paramount importance for the characterisation of underwater signal propagation, particularly in complex environments. Subsequently, through the use of a bidirectional gated recurrent unit (BiGRU), the algorithm is able to effectively capture long-term dependencies in time series data. This enables a more comprehensive analysis and understanding of the changing pattern of signals over time. Furthermore, the incorporation of an attention mechanism within the algorithm enables the model to focus more on the signal features that have a significant impact on localisation, while simultaneously suppressing the interference of extraneous information. This further enhances the efficiency of identifying and utilising the key signal features. ImTSWLS is employed to resolve the position and velocity data following the acquisition of the predicted TDOA/FDOA, thereby enabling the accurate estimation of the position and velocity of the mobile radiation source. The algorithm was subjected to a series of tests in a variety of simulated underwater environments, including different sea states, target motion speeds and base station configurations. The experimental results demonstrate that the algorithm exhibits a deviation of only 2.88 m/s in velocity estimation and 2.58 m in position estimation when the noise level is 20 dB. The algorithm presented in this paper demonstrates superior performance in both position and velocity estimation compared to other algorithms.