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GNSS for Urban Transport Applications

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 35696

Special Issue Editors


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Guest Editor
Department of Components and Systems (COSYS), University Gustave Eiffel, Lille Campus, 59650 Villeneuve d’Ascq, France
Interests: GNSS; transport applications; integrity; multipath; NLOS detection
Special Issues, Collections and Topics in MDPI journals
Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon 999077, Hong Kong
Interests: GNSS; navigation; autonomous systems; sensor fusion; multipath; NLOS
Special Issues, Collections and Topics in MDPI journals
College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga 525-8577, Japan
Interests: machine learning; computer vision; ITS; autonomous driving; GNSS

Special Issue Information

Dear Colleagues,

GNSS solutions are now part of our everyday life. Most of the uses are linked to transport applications, and most often in urban areas, where availability and accuracy are degraded due to signal obstructions, multipath, and NLOS (Non Line Of Sight) reception. Solutions are embedded in cars, autonomous vehicles or fleets of vehicles; drones; public transport systems (buses, trams); as well as smartphone-based solutions.

New and future uses of localization solutions will, however, require a high level of performance in terms of availability and accuracy but also integrity.

To reach high-level performances, new solutions have to be developed. They can rely on the potential of precise positioning, including RTK and PPP that has to be investigated. Special attention has to be paid to algorithms covering GNSS local effect detection, characterization, exclusion or mitigation techniques. Multisensor or hybrid solutions aim to compensate for the degradation of the GNSS. Among new algorithms, one can mention context detection approaches; multiagent collaboration; or uses of environment knowledge based on 3D models, map-matching, or other external sensors such as Camera or LiDAR.

New integrity concepts have to consider these new algorithms and the local errors to properly bound the residual errors.

Lastly, another important issue is also the development of methodologies and tools that are capable of evaluating performances in such areas.

Dr. Juliette Marais
Dr. Li-Ta Hsu
Dr. Yanlei Gu
Guest Editors

Manuscript Submission Information

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Keywords

  • GNSS
  • Urban applications
  • Multipath, NLOS
  • Hybridization, multisensor fusion
  • Detection technics (statistical tests, machine learning, etc.)
  • Performance analysis and enhancement
  • Integrity concepts

Published Papers (14 papers)

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Research

17 pages, 5685 KiB  
Article
Coordinate Frames and Transformations in GNSS Ray-Tracing for Autonomous Driving in Urban Areas
by Kai-Niklas Baasch, Lucy Icking, Fabian Ruwisch and Steffen Schön
Remote Sens. 2023, 15(1), 180; https://doi.org/10.3390/rs15010180 - 29 Dec 2022
Cited by 5 | Viewed by 2596
Abstract
3D Mapping-Aided (3DMA) Global Navigation Satellite System (GNSS) is a widely used method to mitigate multipath errors. Various research has been presented which utilizes 3D building model data in conjunction with ray-tracing algorithms to compute and predict satellites’ visibility conditions and compute delays [...] Read more.
3D Mapping-Aided (3DMA) Global Navigation Satellite System (GNSS) is a widely used method to mitigate multipath errors. Various research has been presented which utilizes 3D building model data in conjunction with ray-tracing algorithms to compute and predict satellites’ visibility conditions and compute delays caused by signal reflection. To simulate, model and potentially correct multipath errors in highly dynamic applications, such as, e.g., autonomous driving, the satellite–receiver–reflector geometry has to be known precisely in a common reference frame. Three-dimensional building models are often provided by regional public or private services and the coordinate information is usually given in a coordinate system of a map projection. Inconsistencies in the coordinate frames used to express the satellite and user coordinates, as well as the reflector surfaces, lead to falsely determined multipath errors and, thus, reduce the performance of 3DMA GNSS. This paper aims to provide the needed transformation steps to consider when integrating 3D building model data, user position, and GNSS orbit information. The impact of frame inconsistencies on the computed extra path delay is quantified based on a simulation study in a local 3D building model; they can easily amount to several meters. Differences between the extra path-delay computations in a metric system and a map projection are evaluated and corrections are proposed to both variants depending on the accuracy needs and the intended use. Full article
(This article belongs to the Special Issue GNSS for Urban Transport Applications)
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18 pages, 6642 KiB  
Article
Innovation-Based Fault Detection and Exclusion Applied to Ultra-WideBand Augmented Urban GNSS Navigation
by Paul Zabalegui, Gorka De Miguel, Jaizki Mendizabal and Iñigo Adin
Remote Sens. 2023, 15(1), 99; https://doi.org/10.3390/rs15010099 - 24 Dec 2022
Cited by 2 | Viewed by 1834
Abstract
Due to their ability to provide a worldwide absolute outdoor positioning, Global Navigation Satellite Systems (GNSS) have become a reference technology in terms of navigation technologies. Transportation-related sectors make use of this technology in order to obtain a position, velocity, and time solution [...] Read more.
Due to their ability to provide a worldwide absolute outdoor positioning, Global Navigation Satellite Systems (GNSS) have become a reference technology in terms of navigation technologies. Transportation-related sectors make use of this technology in order to obtain a position, velocity, and time solution for different outdoor tasks and applications. However, the performance of GNSS-based navigation is degraded when employed in urban areas in which satellite visibility is not good enough or nonexistent, as the ranging signals become obstructed or reflected by any of the numerous surrounding objects. For these situations, Ultra-Wideband (UWB) technology is a perfect candidate to complement GNSS as a navigation solution, as its anchor trilateration-based radiofrequency positioning resembles GNSS’s principle. Nevertheless, this fusion is vulnerable to interferences affecting both systems, since multiple signal-degrading error sources can be found in urban environments. Moreover, an inadequate location of the augmenting UWB transmitters can introduce additional errors to the system due to its vulnerability to the multipath effect. Therefore, the misbehavior of an augmentation system could lead to unexpected and critical faults instead of improving the performance of the standalone GNSS. Accordingly, this research work presents the performance improvement caused by the application of Fault Detection and Exclusion methods when applied to a UWB-augmented low-cost GNSS system in urban environments. Full article
(This article belongs to the Special Issue GNSS for Urban Transport Applications)
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20 pages, 4067 KiB  
Article
Multivehicle 3D Cooperative Positioning Algorithm Based on Information Geometric Probability Fusion of GNSS/Wireless Station Navigation
by Chengkai Tang, Chen Wang, Lingling Zhang, Yi Zhang and Houbing Song
Remote Sens. 2022, 14(23), 6094; https://doi.org/10.3390/rs14236094 - 1 Dec 2022
Cited by 20 | Viewed by 1380
Abstract
With the rapid development of large urban agglomerations and the increasing complexity of urban roads, the high-precision positioning of vehicles has become the cornerstone for the application of vehicle core technologies such as automatic driving. The real-time positioning accuracy of satellite navigation is [...] Read more.
With the rapid development of large urban agglomerations and the increasing complexity of urban roads, the high-precision positioning of vehicles has become the cornerstone for the application of vehicle core technologies such as automatic driving. The real-time positioning accuracy of satellite navigation is easily affected by urban canyons, and its stability is poor; thus, how to use the information of the internet of vehicles to achieve satellite navigation fusion has become a difficult problem of multivehicle cooperative positioning. Aiming at this problem, this paper proposes a multivehicle 3D cooperative positioning algorithm based on information geometric probability fusion of GNSS/wireless station navigation (MVCP-GW), which creatively converts various navigation source information into an information geometric probability model, unifies navigation information time–frequency parameters, and reduces the impact of sudden error. Combined with the Kullback–Leibler algorithm (KLA) fusion method, it breaks off the shackles of the probabilistic two-dimensional model and achieves multivehicle three-dimensional cooperative positioning. Compared with the existing cooperative positioning algorithms in the performance of accuracy stability, applicability, obstruction scenarios, and physical verification, the simulation results and physical verification show that the MVCP-GW algorithm can effectively improve real-time vehicle positioning and the stability of vehicle positioning, as well as resist the impact of obstructed environments. Full article
(This article belongs to the Special Issue GNSS for Urban Transport Applications)
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16 pages, 7232 KiB  
Article
Lidar- and V2X-Based Cooperative Localization Technique for Autonomous Driving in a GNSS-Denied Environment
by Min-Su Kang, Jae-Hoon Ahn, Ji-Ung Im and Jong-Hoon Won
Remote Sens. 2022, 14(22), 5881; https://doi.org/10.3390/rs14225881 - 20 Nov 2022
Cited by 5 | Viewed by 3133
Abstract
Autonomous vehicles are equipped with multiple heterogeneous sensors and drive while processing data from each sensor in real time. Among the sensors, the global navigation satellite system (GNSS) is essential to the localization of the vehicle itself. However, if a GNSS-denied situation occurs [...] Read more.
Autonomous vehicles are equipped with multiple heterogeneous sensors and drive while processing data from each sensor in real time. Among the sensors, the global navigation satellite system (GNSS) is essential to the localization of the vehicle itself. However, if a GNSS-denied situation occurs while driving, the accident risk may be high due to the degradation of the vehicle positioning performance. This paper presents a cooperative positioning technique based on the lidar sensor and vehicle-to-everything (V2X) communication. The ego-vehicle continuously tracks surrounding vehicles and objects, and localizes itself using tracking information from the surroundings, especially in GNSS-denied situations. We present the effectiveness of the cooperative positioning technique by constructing a GNSS-denied case during autonomous driving. A numerical simulation using a driving simulator is included in the paper to evaluate and verify the proposed method in various scenarios. Full article
(This article belongs to the Special Issue GNSS for Urban Transport Applications)
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16 pages, 7334 KiB  
Article
Preparatory Railway Track Geometry Estimation Based on GNSS and IMU Systems
by Slawomir Judek, Andrzej Wilk, Wladysław Koc, Leszek Lewiński, Artur Szumisz, Piotr Chrostowski, Sławomir Grulkowski, Jacek Szmagliński, Michal Michna, Krzysztof Karwowski, Jacek Skibicki and Roksana Licow
Remote Sens. 2022, 14(21), 5472; https://doi.org/10.3390/rs14215472 - 31 Oct 2022
Cited by 4 | Viewed by 1663
Abstract
The article discusses an important issue of railway line construction and maintenance, which fundamentally is the verification of geometric parameters of the railway track. For this purpose, mobile measurements have been performed using a measuring platform with two properly arranged GNSS receivers, which [...] Read more.
The article discusses an important issue of railway line construction and maintenance, which fundamentally is the verification of geometric parameters of the railway track. For this purpose, mobile measurements have been performed using a measuring platform with two properly arranged GNSS receivers, which made it possible to determine the base vector of the platform. The measuring functionality of the system was extended by IMU. In this article, the effect of measuring conditions on the accuracy of the results collected from GNSS receivers is analyzed. In particular, the advisability of digital filtering of the recorded coordinates to eliminate disturbances is indicated. The article also presents the possible use of GNSS devices and the IMU unit for determining the direction angle and the longitudinal and lateral inclination angles of the railway track. This makes it possible to verify the track geometry in the horizontal plane by determining the positions of straight sections, circular arcs, and transition curves. It is indicated that the results of measurements are repeatable despite the dynamic interaction between the railway track and the measuring platform. The results confirm the usefulness of the applied GNSS and IMU signal processing method for monitoring the geometrical parameters of the railway track in operating conditions. Full article
(This article belongs to the Special Issue GNSS for Urban Transport Applications)
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23 pages, 73178 KiB  
Article
IMU-Aided Precise Point Positioning Performance Assessment with Smartphones in GNSS-Degraded Urban Environments
by Hongyu Zhu, Linyuan Xia, Qianxia Li, Jingchao Xia and Yuezhen Cai
Remote Sens. 2022, 14(18), 4469; https://doi.org/10.3390/rs14184469 - 7 Sep 2022
Cited by 8 | Viewed by 2288
Abstract
The tracking of satellite signals with the passive linearly polarized embedded global navigation satellite system (GNSS) antenna of smartphones in dynamic scenarios is susceptible to the changing multipath and obstructions in urban environments, which lead to a significant decrease in the availability and [...] Read more.
The tracking of satellite signals with the passive linearly polarized embedded global navigation satellite system (GNSS) antenna of smartphones in dynamic scenarios is susceptible to the changing multipath and obstructions in urban environments, which lead to a significant decrease in the availability and reliability of GNSS solutions. Accordingly, based on the characteristics of smartphone GNSS and inertial measurement unit (IMU) sensors data in GNSS-degraded environments, we established an IMU-aided uncombined precise point positioning (PPP) mathematical model that is suitable for smartphones. To enhance the reliability of initial alignment in dynamic mode, the step function variances depending on carrier-to-noise density ratio were established with the variances of GNSS measurements, and the inertial navigation system (INS) parameters were initialized while both the velocity of smartphones and the position dilution of precision (PDOP) reached corresponding thresholds. Considering the measurement noise and observations gaps of smartphones, the robust Kalman filter (RKF) with equivalent variance matrix was used for parameter estimation to improve the convergence efficiency of the coupled PPP/INS model. Experimental results indicated that the proposed PPP/INS method can effectively improve the positioning performance of smartphones in GNSS-degraded environments. Compared with the conventional smartphone PPP scheme, the PPP/INS horizontal errors in the eastern and western areas of the long trajectory experiment decreased by 49.37% and 48.29%, respectively. Meanwhile, the trajectory deviation of smartphones can remain stable in the tunnel where GNSS signals are blocked. Full article
(This article belongs to the Special Issue GNSS for Urban Transport Applications)
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34 pages, 13061 KiB  
Article
An Algorithm to Assist the Robust Filter for Tightly Coupled RTK/INS Navigation System
by Zun Niu, Guangchen Li, Fugui Guo, Qiangqiang Shuai and Bocheng Zhu
Remote Sens. 2022, 14(10), 2449; https://doi.org/10.3390/rs14102449 - 20 May 2022
Cited by 5 | Viewed by 2704
Abstract
The Real-Time Kinematic (RTK) positioning algorithm is a promising positioning technique that can provide real-time centimeter-level positioning precision in GNSS-friendly areas. However, the performance of RTK can degrade in GNSS-hostile areas like urban canyons. The surrounding buildings and trees can reflect and block [...] Read more.
The Real-Time Kinematic (RTK) positioning algorithm is a promising positioning technique that can provide real-time centimeter-level positioning precision in GNSS-friendly areas. However, the performance of RTK can degrade in GNSS-hostile areas like urban canyons. The surrounding buildings and trees can reflect and block the Global Navigation Satellite System (GNSS) signals, obstructing GNSS receivers’ ability to maintain signal tracking and exacerbating the multipath effect. A common method to assist RTK is to couple RTK with the Inertial Navigation System (INS). INS can provide accurate short-term relative positioning results. The Extended Kalman Filter (EKF) is usually used to couple RTK with INS, whereas the GNSS outlying observations significantly influence the performance. The Robust Kalman Filter (RKF) is developed to offer resilience against outliers. In this study, we design an algorithm to improve the traditional RKF. We begin by implementing the tightly coupled RTK/INS algorithm and the conventional RKF in C++. We also introduce our specific implementation in detail. Then, we test and analyze the performance of our codes on public datasets. Finally, we propose a novel algorithm to improve RKF and test the improvement. We introduce the Carrier-to-Noise Ratio (CNR) to help detect outliers that should be discarded. The results of the tests show that our new algorithm’s accuracy is improved when compared to the traditional RKF. We also open source the majority of our code, as we find there are few open-source projects for coupled RTK/INS in C++. Researchers can access the codes at our GitHub. Full article
(This article belongs to the Special Issue GNSS for Urban Transport Applications)
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17 pages, 3226 KiB  
Article
Non-Least Square GNSS Positioning Algorithm for Densely Urbanized Areas
by Jerzy Demkowicz
Remote Sens. 2022, 14(9), 2027; https://doi.org/10.3390/rs14092027 - 22 Apr 2022
Cited by 2 | Viewed by 1918
Abstract
The paper introduces an essentially new algorithm for calculating the GNSS position as an alternative to the least-square method. The proposed approach can be widely applied to any positioning method that uses multiple position lines for position calculation and is an example of [...] Read more.
The paper introduces an essentially new algorithm for calculating the GNSS position as an alternative to the least-square method. The proposed approach can be widely applied to any positioning method that uses multiple position lines for position calculation and is an example of how using a numerical solution can improve position accuracy without access to historical data. In essence, the method is based on the adaptation of the median filtering method widely used in the field of image processing, while at the same time applying a combinatorial approach and order statistics. The proposed solution makes it possible to improve on and assess the credibility of a single measurement. The article highlights the differences between the proposed and currently used approaches, as well as their advantages and disadvantages. The algorithm has been extensively tested under various environmental and weather conditions. The tests were carried out in typical and also in very demanding conditions, thus taking into account the real application context, i.e., pedestrian and car navigation in densely urbanized areas. Full article
(This article belongs to the Special Issue GNSS for Urban Transport Applications)
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28 pages, 10643 KiB  
Article
An Improved Adaptive Kalman Filter for a Single Frequency GNSS/MEMS-IMU/Odometer Integrated Navigation Module
by Peihui Yan, Jinguang Jiang, Fangning Zhang, Dongpeng Xie, Jiaji Wu, Chao Zhang, Yanan Tang and Jingnan Liu
Remote Sens. 2021, 13(21), 4317; https://doi.org/10.3390/rs13214317 - 27 Oct 2021
Cited by 13 | Viewed by 2339
Abstract
Aiming at the GNSS receiver vulnerability in challenging urban environments and low power consumption of integrated navigation systems, an improved robust adaptive Kalman filter (IRAKF) algorithm with real-time performance and low computation complexity for single-frequency GNSS/MEMS-IMU/odometer integrated navigation module is proposed. The algorithm [...] Read more.
Aiming at the GNSS receiver vulnerability in challenging urban environments and low power consumption of integrated navigation systems, an improved robust adaptive Kalman filter (IRAKF) algorithm with real-time performance and low computation complexity for single-frequency GNSS/MEMS-IMU/odometer integrated navigation module is proposed. The algorithm obtains the scale factor by the prediction residual, and uses it to adjust the artificially set covariance matrix of the observation vector under different GNSS solution states, so that the covariance matrix of the observation vector changes continuously with the complex scene. Then, the adaptive factor is calculated by the Mahalanobis distance to inflate the state prediction covariance matrix. In addition, the one-step prediction Kalman filter is introduced to reduce the computational complexity of the algorithm. The performance of the algorithm is verified by vehicle experiments in the challenging urban environments. Experiments show that the algorithm can effectively weaken the effects of abnormal model deviations and outliers in the measurements and improve the positioning accuracy of real-time integrated navigation. It can meet the requirements of low power consumption real-time vehicle navigation applications in the complex urban environment. Full article
(This article belongs to the Special Issue GNSS for Urban Transport Applications)
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32 pages, 5820 KiB  
Article
Dynamic Adaptive Low Power Adjustment Scheme for Single-Frequency GNSS/MEMS-IMU/Odometer Integrated Navigation in the Complex Urban Environment
by Peihui Yan, Jinguang Jiang, Yanan Tang, Fangning Zhang, Dongpeng Xie, Jiaji Wu, Jianghua Liu and Jingnan Liu
Remote Sens. 2021, 13(16), 3236; https://doi.org/10.3390/rs13163236 - 15 Aug 2021
Cited by 11 | Viewed by 2316
Abstract
Positioning accuracy and power consumption are essential performance indicators of integrated navigation and positioning chips. This paper proposes a single-frequency GNSS/MEMS-IMU/odometer real-time high-precision integrated navigation algorithm with dynamic power adaptive adjustment capability in complex environments. It is implemented in a multi-sensor fusion navigation [...] Read more.
Positioning accuracy and power consumption are essential performance indicators of integrated navigation and positioning chips. This paper proposes a single-frequency GNSS/MEMS-IMU/odometer real-time high-precision integrated navigation algorithm with dynamic power adaptive adjustment capability in complex environments. It is implemented in a multi-sensor fusion navigation SiP (system in package) chip. The simplified INS algorithm and the simplified Kalman filter algorithm are adopted to reduce the computation load, and the strategy of adaptively adjusting the data rate and selecting the observation information for measurement update in different scenes and motion modes is combined to realize high-precision positioning and low power consumption in complex scenes. The performance of the algorithm is verified by real-time vehicle experiments in a variety of complex urban environments. The results show that the RMS statistical value of the overall positioning error in the entire road section is 0.312 m, and the overall average power consumption is 141 mW, which meets the requirements of real-time integrated navigation for high-precision positioning and low power consumption. It supports single-frequency GNSS/MEMS-IMU/odometer integrated navigation SiP chip in real-time, high-precision, low-power, and small-volume applications. Full article
(This article belongs to the Special Issue GNSS for Urban Transport Applications)
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21 pages, 1736 KiB  
Article
Precision-Aided Partial Ambiguity Resolution Scheme for Instantaneous RTK Positioning
by Juan Manuel Castro-Arvizu, Daniel Medina, Ralf Ziebold, Jordi Vilà-Valls, Eric Chaumette and Pau Closas
Remote Sens. 2021, 13(15), 2904; https://doi.org/10.3390/rs13152904 - 23 Jul 2021
Cited by 7 | Viewed by 2604
Abstract
The use of carrier phase data is the main driver for high-precision Global Navigation Satellite Systems (GNSS) positioning solutions, such as Real-Time Kinematic (RTK). However, carrier phase observations are ambiguous by an unknown number of cycles, and their use in RTK relies on [...] Read more.
The use of carrier phase data is the main driver for high-precision Global Navigation Satellite Systems (GNSS) positioning solutions, such as Real-Time Kinematic (RTK). However, carrier phase observations are ambiguous by an unknown number of cycles, and their use in RTK relies on the process of mapping real-valued ambiguities to integer ones, so-called Integer Ambiguity Resolution (IAR). The main goal of IAR is to enhance the position solution by virtue of its correlation with the estimated integer ambiguities. With the deployment of new GNSS constellations and frequencies, a large number of observations is available. While this is generally positive, positioning in medium and long baselines is challenging due to the atmospheric residuals. In this context, the process of solving the complete set of ambiguities, so-called Full Ambiguity Resolution (FAR), is limiting and may lead to a decreased availability of precise positioning. Alternatively, Partial Ambiguity Resolution (PAR) relaxes the condition of estimating the complete vector of ambiguities and, instead, finds a subset of them to maximize the availability. This article reviews the state-of-the-art PAR schemes, addresses the analytical performance of a PAR estimator following a generalization of the Cramér–Rao Bound (CRB) for the RTK problem, and introduces Precision-Driven PAR (PD-PAR). The latter constitutes a new PAR scheme which employs the formal precision of the (potentially fixed) positioning solution as selection criteria for the subset of ambiguities to fix. Numerical simulations are used to showcase the performance of conventional FAR and FAR approaches, and the proposed PD-PAR against the generalized CRB associated with PAR problems. Real-data experimental analysis for a medium baseline complements the synthetic scenario. The results demonstrate that (i) the generalization for the RTK CRB constitutes a valid lower bound to assess the asymptotic behavior of PAR estimators, and (ii) the proposed PD-PAR technique outperforms existing FAR and PAR solutions as a non-recursive estimator for medium and long baselines. Full article
(This article belongs to the Special Issue GNSS for Urban Transport Applications)
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31 pages, 7098 KiB  
Article
Feature-Aided RTK/LiDAR/INS Integrated Positioning System with Parallel Filters in the Ambiguity-Position-Joint Domain for Urban Environments
by Wenyi Li, Gang Liu, Xiaowei Cui and Mingquan Lu
Remote Sens. 2021, 13(10), 2013; https://doi.org/10.3390/rs13102013 - 20 May 2021
Cited by 9 | Viewed by 2659
Abstract
As the modern navigation business evolves, demands for high-precision positioning in GNSS-challenged environments increase, and the integrated system composed of Global Navigation Satellite System (GNSS)-based Real-Time Kinematic (RTK), inertial system (INS), Light Detection and Ranging (LiDAR), etc., is accepted as the most feasible [...] Read more.
As the modern navigation business evolves, demands for high-precision positioning in GNSS-challenged environments increase, and the integrated system composed of Global Navigation Satellite System (GNSS)-based Real-Time Kinematic (RTK), inertial system (INS), Light Detection and Ranging (LiDAR), etc., is accepted as the most feasible solution to the issue. For prior-map-free situations, as the only sensor with a global frame, RTK determines and maintains the global positioning precision of the integrated system. However, RTK performance degrades greatly in GNSS-challenged environments, and most of the existing integrated systems adopt loose coupling mode, which does nothing to improve RTK and, thus, prevents integrated systems from further improvement. Aiming at improving RTK performance in the RTK/LiDAR/INS integrated system, we proposed an innovative integrated algorithm that utilizes RTK to register LiDAR features while integrating the pre-registered LiDAR features to RTK and adopts parallel filters in the ambiguity-position-joint domain to weaken the effects of low satellite availability, cycle slips, and multipath. By doing so, we can improve the RTK fix rate and stability in GNSS-challenged environments. The results of the theoretical analyses, simulation experiments, and a road test proved that the proposed method improved RTK performance in GNSS-challenged environments and, thus, guaranteed the global positioning precision of the whole system. Full article
(This article belongs to the Special Issue GNSS for Urban Transport Applications)
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36 pages, 16076 KiB  
Article
GNSS RUMS: GNSS Realistic Urban Multiagent Simulator for Collaborative Positioning Research
by Guohao Zhang, Bing Xu, Hoi-Fung Ng and Li-Ta Hsu
Remote Sens. 2021, 13(4), 544; https://doi.org/10.3390/rs13040544 - 3 Feb 2021
Cited by 19 | Viewed by 3676
Abstract
Accurate localization of road agents (GNSS receivers) is the basis of intelligent transportation systems, which is still difficult to achieve for GNSS positioning in urban areas due to the signal interferences from buildings. Various collaborative positioning techniques were recently developed to improve the [...] Read more.
Accurate localization of road agents (GNSS receivers) is the basis of intelligent transportation systems, which is still difficult to achieve for GNSS positioning in urban areas due to the signal interferences from buildings. Various collaborative positioning techniques were recently developed to improve the positioning performance by the aid from neighboring agents. However, it is still challenging to study their performances comprehensively. The GNSS measurement error behavior is complicated in urban areas and unable to be represented by naive models. On the other hand, real experiments requiring numbers of devices are difficult to conduct, especially for a large-scale test. Therefore, a GNSS realistic urban measurement simulator is developed to provide measurements for collaborative positioning studies. The proposed simulator employs a ray-tracing technique searching for all possible interferences in the urban area. Then, it categorizes them into direct, reflected, diffracted, and multipath signal to simulate the pseudorange, C/N0, and Doppler shift measurements correspondingly. The performance of the proposed simulator is validated through real experimental comparisons with different scenarios based on commercial-grade receivers. The proposed simulator is also applied with different positioning algorithms, which verifies it is sophisticated enough for the collaborative positioning studies in the urban area. Full article
(This article belongs to the Special Issue GNSS for Urban Transport Applications)
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23 pages, 1049 KiB  
Article
Joint Delay-Doppler Estimation Performance in a Dual Source Context
by Corentin Lubeigt, Lorenzo Ortega, Jordi Vilà-Valls, Laurent Lestarquit and Eric Chaumette
Remote Sens. 2020, 12(23), 3894; https://doi.org/10.3390/rs12233894 - 27 Nov 2020
Cited by 11 | Viewed by 2111
Abstract
Evaluating the time-delay, Doppler effect and carrier phase of a received signal is a challenging estimation problem that was addressed in a large variety of remote sensing applications. This problem becomes more difficult and less understood when the signal is reflected off one [...] Read more.
Evaluating the time-delay, Doppler effect and carrier phase of a received signal is a challenging estimation problem that was addressed in a large variety of remote sensing applications. This problem becomes more difficult and less understood when the signal is reflected off one or multiple surfaces and interferes with itself at the receiver stage. This phenomenon might deteriorate the overall system performance, as for the multipath effect in Global Navigation Satellite Systems (GNSS), and mitigation strategies must be accounted for. In other applications such as GNSS reflectometry (GNSS-R) it may be interesting to estimate the parameters of the reflected signal to deduce the geometry and the surface characteristics. In either case, a better understanding of this estimation problem is directly brought by the corresponding lower performance bounds. In the high signal-to-noise ratio regime of the Gaussian conditional signal model, the Cramér-Rao bound (CRB) provides an accurate lower bound in the mean square error sense. In this article, we derive a new compact CRB expression for the joint time-delay and Doppler estimation in a dual source context, considering a band-limited signal and its specular reflection. These compact CRBs are expressed in terms of the baseband signal samples, making them especially easy to use whatever the baseband signal considered, therefore being valid for a variety of remote sensors. This extends existing results in the single source context and opens the door to a plethora of usages to be discussed in the article. The proposed CRB expressions are validated in two representative navigation and radar examples. Full article
(This article belongs to the Special Issue GNSS for Urban Transport Applications)
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