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GNSS for Urban Transport Applications II
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GNSS for Urban Transport Applications II

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

Deadline for manuscript submissions: 31 January 2025 | Viewed by 13134

Special Issue Editors

Dr. Juliette Marais

E-Mail Website
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
Dr. Li-Ta Hsu

E-Mail Website
Guest Editor
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
Dr. Omar García Crespillo

E-Mail Website
Guest Editor
Institute of Communications and Navigation, German Aerospace Center (DLR), Oberpfaffenhofen-Wessling, Germany
Interests: GNSS; integrity monitoring; inertial navigation; error modelling; Kalman filtering

Special Issue Information

Dear Colleagues,

This Special Issue is a sequel of a previous Special Issue entitled “GNSS for Urban Transport Applications”. I would like to thank all authors and coauthors of the previous edition who supported the first volume in becoming a grand success.

GNSS positioning and timing solutions are now part of our everyday life, with most of their uses linked to transport applications, particularly in urban areas where GNSS availability and accuracy tend to be degraded due to signal obstructions, multipath, NLOS (non-line-of-sight) signal reception and interferences. Solutions are embedded in cars, autonomous vehicles or fleets of vehicles, drones, public transport systems (buses and trams), as well as smartphone-based solutions.

However, future uses of GNSS localization solutions are predicted to require novel levels of performance in terms of accuracy, availability, robustness and integrity.

In order to reach novel performance levels in urban environments, innovative approaches and solutions still have to be investigated and developed. Real-time kinematic (RTK) and precise point positioning (PPP) solutions are capable of providing more accurate positioning through the exploitation of carrier phases. As the availability of ground RTK stations increases, and with the new high-accuracy service (HAS) of Galileo, these alternatives can be more accessible, and should be investigated for applications in urban environments. However, many challenges remain in regard to ensuring their robustness, assess their integrity and ensure availability with shorter convergence times. Special attention should also be paid to innovative algorithms covering GNSS local effect characterization, detection and exclusion or mitigation as the basis to increase trust in GNSS in challenging scenarios. Multisensor or hybrid solutions aim to complement or compensate for the degradation of the GNSS. Among novel algorithms, one can mention context detection approaches, multiagent collaboration and the use of environment knowledge based on 3D models, map-matching and other external sensors, such as cameras or LiDAR.

Novel integrity concepts need to consider these novel algorithms and local errors to properly bound the residual errors. This must be extended to multisensor solutions for many ground transportation applications. An integrity assessment is essential for future safety-related applications, such as autonomous driving, railway signalling and urban air mobility (UAM).

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

Dr. Juliette Marais
Dr. Li-Ta Hsu
Dr. Omar García Crespillo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • GNSS
  • urban applications
  • multipath
  • NLOS
  • hybridization
  • multisensor fusion
  • detection techniques (statistical tests, machine learning, etc.)
  • performance analysis and enhancement
  • integrity concepts

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Published Papers (7 papers)

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Research

24 pages, 18779 KiB  
Article
An Improved Velocity-Aided Method for Smartphone Single-Frequency Code Positioning in Real-World Driving Scenarios
by Zhaowei Han, Xiaoming Wang, Jinglei Zhang, Shiji Xin, Qiuying Huang and Sizhe Shen
Remote Sens. 2024, 16(21), 3988; https://doi.org/10.3390/rs16213988 - 27 Oct 2024
Viewed by 941
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue GNSS for Urban Transport Applications II)
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Figure 1

Figure 1
<p>Flowchart of the KFSPP-P processing procedure.</p> Full article ">Figure 2
<p>Installation method of smartphones in the vehicle. From left to right are S21, CL8, and AD11. The dashcam is shown in the upper left corner.</p> Full article ">Figure 3
<p>The experimental route trajectory is shown on the left, with blue, red, green, and yellow corresponding to open-sky road, suburban, tree-lined road, and urban environments, respectively. The right image depicts the actual environments corresponding to open-sky road (<b>A</b>), suburban (<b>B</b>), tree-lined road (<b>C</b>), and urban (<b>D</b>).</p> Full article ">Figure 4
<p>The schematic diagram of the in-vehicle experimental setup. The lever arm of the smartphone to GNSS antenna is front = 2.78 m, right = 0.43 m, and up = 0.66 m; the lever arm of the smartphone to ISA100C is front = 3.48 m, right = 0.13 m, and up = 0.3 m.</p> Full article ">Figure 5
<p>Number of satellites and PDOP values for the S21 on the test route. The color blocks located at the bottom of the image represent changes in environmental scenes: blue, red, green, and yellow correspond to open-sky road (A), suburban (B), tree-lined road (C), and urban (D), respectively.</p> Full article ">Figure 6
<p>The number of code (red), Doppler (blue), and phase (green) observations recorded by the S21 smartphone along the experimental trajectory (<b>left</b>); the average number of each observation type in open-sky road (A), suburban (B), tree-lined road (C), and urban (D) environments (<b>right</b>).</p> Full article ">Figure 7
<p>TDCMC statistics for GPS, Galileo, BDS, and GLONASS systems recorded by the S21 smartphone along the experimental trajectory, with different colors representing individual satellites.</p> Full article ">Figure 8
<p>Distribution of post-fit residuals during Doppler-based velocity estimation using the S21, showing results without (<b>top</b>) and with (<b>bottom</b>) robust estimation algorithms applied, with different colors representing different satellites. Notably, the y-axis scale range is −4 to 4 m/s (<b>top</b>) and −0.4 to 0.4 m/s (<b>bottom</b>). The color blocks located at the bottom of the image represent changes in environmental scenes: blue, red, green, and yellow correspond to open-sky road (A), suburban (B), tree-lined road (C), and urban (D), respectively.</p> Full article ">Figure 9
<p>Doppler velocity estimation errors for the S21, without (red) and with (blue) the robust estimation algorithm applied. The color blocks located at the bottom of the image represent changes in environmental scenes: blue, red, green, and yellow correspond to open-sky road (A), suburban (B), tree-lined road (C), and urban (D), respectively.</p> Full article ">Figure 10
<p>Time series of velocity errors for the S21, CL8, and AD11 using LS-D, LS-T, LS-DT, KF-DT1, and KF-DT2 solutions. The red, blue, and green lines represent the velocity errors in the E, N, and U directions, respectively. Here, the velocity errors in the E and U directions are presented with y = 2.5 and y = −2.5 as the respective reference baselines for the vertical axis.</p> Full article ">Figure 11
<p>Error plots for the S21, CL8, and AD11 in the E, N, and U directions using the SPP (red), KFSPP-V (blue), and KFSPP-P (green) solutions.</p> Full article ">Figure 12
<p>Four typical environments, with real routes (<b>a</b>–<b>d</b>) corresponding to open-sky road (A—blue), suburban (B—red), tree-lined road (C—green), and urban (D—yellow).</p> Full article ">Figure 13
<p>Positioning errors in the E, N, and U directions for the S21 smartphone across four routes (<b>a</b>–<b>d</b>). The red, blue, and green lines represent the SPP, KFSPP-V, and KFSPP-P solutions, respectively.</p> Full article ">
20 pages, 3928 KiB  
Article
On the Use of Ultra-WideBand-Based Augmentation for Precision Maneuvering
by Paul Zabalegui, Gorka De Miguel, Nerea Fernández-Berrueta, Joanes Aizpuru, Jaizki Mendizabal and Iñigo Adín
Remote Sens. 2024, 16(5), 911; https://doi.org/10.3390/rs16050911 - 4 Mar 2024
Cited by 2 | Viewed by 1545
Abstract
The limitations of the existing Global Navigation Satellite Systems (GNSS) integrated with Inertial Measurement Units (IMU) have presented significant challenges in meeting the stringent demands of precision maneuvering. The identified constraints in terms of accuracy and availability have required the development of an [...] Read more.
The limitations of the existing Global Navigation Satellite Systems (GNSS) integrated with Inertial Measurement Units (IMU) have presented significant challenges in meeting the stringent demands of precision maneuvering. The identified constraints in terms of accuracy and availability have required the development of an alternative solution to enhance the performance of navigation systems in dynamic and diverse environments. This paper summarizes the research regarding the integration of ultra-wideband (UWB) technology as an augmentation of the conventional GNSS+IMU system; it proposes an approach that aims to overcome the limitations of conventional navigation systems. By making use of UWB technology, the proposed low-cost UWB-augmented GNSS+IMU system not only fulfils the required performance standards but also offers the unique capability to navigate seamlessly across indoor and outdoor environments. The developed system was validated through comprehensive testing and analysis in both the automotive and maritime sectors. The obtained results highlight the system’s capacity as a dependable and resilient solution for precise navigation, and they promote its use within the domain of accurate maneuvering. Full article
(This article belongs to the Special Issue GNSS for Urban Transport Applications II)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Kalman filter state uncertainty convergence process [<a href="#B26-remotesensing-16-00911" class="html-bibr">26</a>].</p> Full article ">Figure 2
<p>High–level view of a tightly coupled architecture based on a GNSS+IMU system [<a href="#B26-remotesensing-16-00911" class="html-bibr">26</a>].</p> Full article ">Figure 3
<p>Lower-view tightly coupled INS/GNSS integration architecture with open- and closed-loop variations [<a href="#B26-remotesensing-16-00911" class="html-bibr">26</a>].</p> Full article ">Figure 4
<p>Employed measurement system (Red: GNSS antenna. Green: navigation system. Orange: power supply. Yellow: ground truth generating system).</p> Full article ">Figure 5
<p>Onboard measurement setup for the maritime measurement campaign.</p> Full article ">Figure 6
<p>Onboard measurement setup for the automotive measurement campaign.</p> Full article ">Figure 7
<p>Test scenario: low visibility and indoor areas. Closed-shape distribution of the UWB anchors within the area. (<b>Upper</b>) Location of the anchors relative to the start/end position. (<b>Lower</b>) Location of the anchors and the ground truth in the test scenario.</p> Full article ">Figure 8
<p>Example of the visibility of UWB anchors in the automotive measurement.</p> Full article ">Figure 9
<p>Test scenario: suburban environment with surrounding metallic objects and buildings. L-shaped distribution of the UWB anchors within the area. (<b>Upper</b>) Location of the anchors relative to the start/end position. (<b>Lower</b>) Location of the anchors and the ground truth in the test scenario.</p> Full article ">Figure 10
<p>Example of the visibility of UWB anchors in the maritime measurement.</p> Full article ">
23 pages, 8041 KiB  
Article
A Comparative Study of Factor Graph Optimization-Based and Extended Kalman Filter-Based PPP-B2b/INS Integrated Navigation
by Shiji Xin, Xiaoming Wang, Jinglei Zhang, Kai Zhou and Yufei Chen
Remote Sens. 2023, 15(21), 5144; https://doi.org/10.3390/rs15215144 - 27 Oct 2023
Cited by 9 | Viewed by 2332
Abstract
Recently, factor graph optimization (FGO)-based GNSS/INS integrated navigation has garnered widespread attention for its ability to provide more robust positioning performance in challenging environments like urban canyons, compared to traditional extended Kalman filter (EKF)-based methods. In existing GNSS/INS integrated navigation methods based on [...] Read more.
Recently, factor graph optimization (FGO)-based GNSS/INS integrated navigation has garnered widespread attention for its ability to provide more robust positioning performance in challenging environments like urban canyons, compared to traditional extended Kalman filter (EKF)-based methods. In existing GNSS/INS integrated navigation methods based on FGO, the primary approach involves combining single point positioning (SPP) or real-time kinematic (RTK) with INS by constructing factors between consecutive epochs to resist outliers and achieve robust positioning. However, the potential of a high-precision positioning system based on the FGO algorithm, combining INS and PPP-B2b and that does not rely on reference stations and network connections, has not been fully explored. In this study, we developed a loosely coupled PPP-B2b/INS model based on the EKF and FGO algorithms. Experiments in different urban road and overpass scenarios were conducted to investigate the positioning performance of the two different integration navigation algorithms using different degrades of inertial measurement units (IMUs). The results indicate that the FGO algorithm outperforms the EKF algorithm in terms of positioning with the combination of GNSS and different degrades of IMUs under various conditions. Compared to the EKF method, the application of the FGO algorithm leads to improvements in the positioning accuracy of approximately 15.8%~45.9% and 19%~41.3% in horizontal and vertical directions, respectively, for different experimental conditions. In scenarios with long and frequent signal obstructions, the advantages of the FGO algorithm become more evident, especially in the horizontal direction. An obvious improvement in positioning results is observed when the tactical-grade IMU is used instead of the microelectron-mechanical system (MEMS) IMU in the GNSS/INS combination, which is more evident for the FGO algorithm than for the EKF algorithm. Full article
(This article belongs to the Special Issue GNSS for Urban Transport Applications II)
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Figure 1

Figure 1
<p>Flowchart of the system for PPP-B2b/INS integration based on the EKF.</p> Full article ">Figure 2
<p>Flowchart of the system for PPP-B2b/INS integration based on the FGO.</p> Full article ">Figure 3
<p>Trajectories of two vehicle experiments. (<b>A</b>,<b>B</b>) Experiment A conduct from 371,514 to 374,911 s of 2269 week in GPS time; Experiment B conduct from 375,476 to 377,401 s of 2269 week in GPS time.</p> Full article ">Figure 4
<p>Illustration of the experimental equipment.</p> Full article ">Figure 5
<p>Positioning results series of PPP–B2b and EKF and FGO PPP–B2b/INS integrated navigation are, respectively, presented (Experiment A). The NSAT and PDOP series are also presented in the figure.</p> Full article ">Figure 6
<p>Three typical car driving scenarios in Experiment A (Number I–III represents three different observation environments).</p> Full article ">Figure 7
<p>Positioning error series in three typical scenarios and the overall route, along with the driving trajectories. The trajectories for the three typical scenarios are marked on the trajectory plot.</p> Full article ">Figure 8
<p>Another two typical car driving scenarios of Experiment B (Number IV and V represents two different challenging environments).</p> Full article ">Figure 9
<p>Positioning results series of PPP–B2b and EKF and FGO PPP–B2b/INS integrated navigation, respectively (Experiment B). The NSAT and PDOP series are also presented in the figure.</p> Full article ">Figure 10
<p>Positioning error series of the overall route, along with the driving trajectories. The trajectories for the two typical scenarios are marked on the trajectory plot.</p> Full article ">Figure 11
<p>Positioning error series of PPP-B2b/T-INS and PPP-B2b/MEMS in two typical scenarios.</p> Full article ">Figure 12
<p>Positioning error series of EKF and FGO with window sizes of 1 and 10 in Experiment B.</p> Full article ">
22 pages, 14088 KiB  
Article
Predicting C/N0 as a Key Parameter for Network RTK Integrity Prediction in Urban Environments
by Ali Karimidoona and Steffen Schön
Remote Sens. 2023, 15(19), 4850; https://doi.org/10.3390/rs15194850 - 7 Oct 2023
Viewed by 1148
Abstract
Autonomuous transportation systems require navigation performance with a high level of integrity. As Global Navigation Satellite System (GNSS) real-time kinematic (RTK) solutions are needed to ensure lane level accuracy of the whole system, these solutions should be trustworthy, which is often not the [...] Read more.
Autonomuous transportation systems require navigation performance with a high level of integrity. As Global Navigation Satellite System (GNSS) real-time kinematic (RTK) solutions are needed to ensure lane level accuracy of the whole system, these solutions should be trustworthy, which is often not the case in urban environments. Thus, the prediction of integrity for specific routes or trajectories is of interest. The carrier-to-noise density ratio (C/N0) reported by the GNSS receiver offers important insights into the signal quality, the carrier phase availability and subsequently the RTK solution integrity. The ultimate goal of this research is to investigate the predictability of the GNSS signal strength. Using a ray-tracing algorithm together with known satellite positions and 3D building models, not only the satellite visibility but also the GNSS signal propagation conditions at waypoints along an intended route are computed. Including antenna gain, free-space propagation as well as reflection and diffraction at surfaces and vegetation, the predicted C/N0 is compared to that recorded by an Septentrio Altus receiver during an experiment in an urban environment in Hannover. Although the actual gain pattern of the receiving antenna was unknown, good agreements were found with small offsets between measured and predicted C/N0. Full article
(This article belongs to the Special Issue GNSS for Urban Transport Applications II)
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Figure 1

Figure 1
<p>Overview of the kinematic experiment. (<b>a</b>) Eight-shaped trajectory driven in an urban environment of Hannover near Leibniz University campus; A to K are the way-points which show the direction of the drive; this trajectory includes parts with quite open sky situations in the parking lot (J to A), and some parts with very difficult sky visibility (D to F). Different colors of way-points are merely chosen for better visibility of the image. (<b>b</b>) Experimental set-up with RTK receivers mounted on top of the van. (<b>c</b>) Installation of the navigation-grade IMU (iMAR iNAT-RQT-4003) inside the van.</p> Full article ">Figure 2
<p>Estimated trajectory from the Altus receiver in the fifth round of the experiment. Different colors representing the four solution types. Fixed solutions are depicted in light green stars. The other solutions are depicted as circles which are centered at the horizontal solution and their radii show the 2D coordinate quality. In the legend, the circles have a radius of 3 m as a scale for the circles in the figure. The arrows indicate the direction of the driving. The way-points A to K are as defined in <a href="#remotesensing-15-04850-f001" class="html-fig">Figure 1</a>a.</p> Full article ">Figure 3
<p>Number of dual-frequency phase observations for GPS (G) plus GLONASS (R) color-coded by solution status. The way-points A to K are as defined in <a href="#remotesensing-15-04850-f001" class="html-fig">Figure 1</a>a.</p> Full article ">Figure 4
<p>Histogram of available dual-frequency (L1 and L2) phase GPS (G) and GLONASS (R) signals for four types of position solutions: (<b>a</b>) fixed, (<b>b</b>) float, (<b>c</b>) code and (<b>d</b>) navigated in all twelve rounds.</p> Full article ">Figure 5
<p>Signal strength variation for GPS and GLONASS L1 signals shown for Round 5. The color code in the bottom of the figure indicate the solution type: fixed (green), float (olive), code (yellow) and navigated (red), cf. <a href="#remotesensing-15-04850-f002" class="html-fig">Figure 2</a>. The way-points A to K are as defined in <a href="#remotesensing-15-04850-f001" class="html-fig">Figure 1</a>a.</p> Full article ">Figure 6
<p>Classification of GNSS signal reception conditions. (<b>a</b>) Satellite signals may experience one of the four situations: LOS, NLOS, multipath or blocked. Diffraction can occur in addition to any of the aforementioned cases. (<b>b</b>) Exemplary skyplot with corresponding visibility status for every azimuth and elevation. This skyplot belongs to a point in the middle of the north–south oriented street near way-point E (cf. <a href="#remotesensing-15-04850-f001" class="html-fig">Figure 1</a>a).</p> Full article ">Figure 7
<p>Predicted and observed GPS satellite visibility exemplarily shown for the fifth round of the experiment: (<b>a</b>) The predicted GPS visibility, the LOS, NLOS, MP and diffracted signals are depicted in green, purple, yellow and gray color, respectively. (<b>b</b>) The real visibility of code GC1C signal. (<b>c</b>) The real visibility of the GL1C signal. The real data are exemplary from the Altus receiver. Short vertical red lines indicate the signal start or interruptions. The way-points A to K are as defined in <a href="#remotesensing-15-04850-f001" class="html-fig">Figure 1</a>a.</p> Full article ">Figure 8
<p>Predicted and observed GLONASS satellite visibility exemplarily shown for the fifth round: (<b>a</b>) The predicted GLONASS visibility, the LOS, NLOS, MP and diffracted signals are depicted in green, purple, yellow and gray color, respectively. (<b>b</b>) The real visibility of code RC1C signal. (<b>c</b>) The real visibility of the RL1C signal. The real data are exemplary from the Altus receiver. Short vertical red lines indicate the signal start or interruption. The way-points A to K are as defined in <a href="#remotesensing-15-04850-f001" class="html-fig">Figure 1</a>a.</p> Full article ">Figure 9
<p>Investigation of the receiver antenna gain pattern (exemplary for high-elevated GPS PRN 1): (<b>a</b>) Real C/N<math display="inline"><semantics> <msub> <mrow/> <mn>0</mn> </msub> </semantics></math> from the Altus receiver, theoretical C/N<math display="inline"><semantics> <msub> <mrow/> <mn>0</mn> </msub> </semantics></math> from simulation using gain pattern of GNSS 800 series antenna, and the gain pattern of the GNSS 800 series antenna. (<b>b</b>) Real C/N<math display="inline"><semantics> <msub> <mrow/> <mn>0</mn> </msub> </semantics></math> from the Altus receiver, theoretical C/N<math display="inline"><semantics> <msub> <mrow/> <mn>0</mn> </msub> </semantics></math> from simulation using modified gain pattern and the modified version of the gain pattern of the GNSS 800 series antenna.</p> Full article ">Figure 10
<p>Real position of one of the trees (<b>right</b> and <b>middle</b>) and the 3D model used to predict the signal fading by the foliage (<b>left</b>).</p> Full article ">Figure 11
<p>Flowchart summarizing the steps to predict C/N<math display="inline"><semantics> <msub> <mrow/> <mn>0</mn> </msub> </semantics></math>.</p> Full article ">Figure 12
<p>The observed and predicted C/N<math display="inline"><semantics> <msub> <mrow/> <mn>0</mn> </msub> </semantics></math> in Round 5 for GPS constellation: (<b>a</b>) PRN 3, (<b>b</b>) PRN 4, (<b>c</b>) PRN 17. The lower bar (at 0) shows the solution status (cf. <a href="#remotesensing-15-04850-f002" class="html-fig">Figure 2</a>a) and the upper bar (at 10) indicates the visibility status (cf. <a href="#remotesensing-15-04850-f005" class="html-fig">Figure 5</a>). The way-points A to K in subfigures a, b and c, are as defined in <a href="#remotesensing-15-04850-f001" class="html-fig">Figure 1</a>a. (<b>d</b>) Shows the sky plot of the GPS satellites during the round.</p> Full article ">Figure 13
<p>The observed and predicted C/N<math display="inline"><semantics> <msub> <mrow/> <mn>0</mn> </msub> </semantics></math> in Round 5 for GPS constellation. (<b>a</b>) PRN 19, (<b>b</b>) PRN 22, (<b>c</b>) PRN 32. The lower bar (at 0) shows the solution status (cf. <a href="#remotesensing-15-04850-f002" class="html-fig">Figure 2</a>a) and the upper bar (at 10) indicates the visibility status (cf. <a href="#remotesensing-15-04850-f005" class="html-fig">Figure 5</a>). The way-points A to K in subfigures a, b and c, are as defined in <a href="#remotesensing-15-04850-f001" class="html-fig">Figure 1</a>a. (<b>d</b>) Shows the sky plot of the GPS satellites during the round.</p> Full article ">Figure 14
<p>Histogram of the difference between the predicted and real C/N<math display="inline"><semantics> <msub> <mrow/> <mn>0</mn> </msub> </semantics></math> values for four different visibility statuses.</p> Full article ">
30 pages, 11526 KiB  
Article
A Multi-Correlation Peak Phase Deblurring Algorithm for BeiDou B1C Signals in Urban Environments
by Xu Yang, Wenquan Feng, Chen Zhuang, Qiang Wang, Xu Yang and Zhe Yang
Remote Sens. 2023, 15(17), 4300; https://doi.org/10.3390/rs15174300 - 31 Aug 2023
Cited by 2 | Viewed by 1458
Abstract
With the widespread global application of BeiDou navigation, BeiDou B1C signaling based on Quadrature Multiplexed Binary Offset Carrier (QMBOC) modulation is expected to be extensively used in urban environments due to its wider signal bandwidth, smaller code pseudorange measurement errors, and stronger multipath [...] Read more.
With the widespread global application of BeiDou navigation, BeiDou B1C signaling based on Quadrature Multiplexed Binary Offset Carrier (QMBOC) modulation is expected to be extensively used in urban environments due to its wider signal bandwidth, smaller code pseudorange measurement errors, and stronger multipath capabilities. Despite offering higher positioning accuracy and secondary modulation characteristics of the BeiDou, B1C signals introduce the challenge of multiple peaks in the autocorrelation function. This leads to phase ambiguity during signal acquisition and tracking, resulting in positioning deviations of tens or even hundreds of meters. In urban environments, such deviations give rise to significant practical application issues. To address this problem, we have designed a multi-loop structure for the synchronous tracking of B1C signals and proposed a multi-peak phase-deblurring algorithm specifically tailored for the BeiDou B1C signal in urban environments. This algorithm considers the coupling relationship between the code and the carrier loops, and by matching the structural design of multiple loops, it achieves a precise and unambiguous phase estimation of the pseudocode, enabling the stable tracking of the entire loop for the BeiDou B1C signal. Simulation and actual testing demonstrate that the algorithm exhibits an error less than 0.03 for chip intervals when the signal-to-noise ratio is greater than −20 dB. Additionally, the accuracy can be improved by adjusting the set conditions, making it suitable for urban environments. Full article
(This article belongs to the Special Issue GNSS for Urban Transport Applications II)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The Research Logic and Analysis Paradigm for the BeiDou B1C Signal and Synchronization Loop.</p> Full article ">Figure 2
<p>(<b>a</b>) Diagram of BeiDou-2 Signal Distribution. (<b>b</b>) Diagram of BeiDou-3 Signal Distribution.</p> Full article ">Figure 3
<p>Comparison of Autocorrelation Functions for Different Modulation Modes.</p> Full article ">Figure 4
<p>Power Spectral Density (PSD) Function of BOC Signal.</p> Full article ">Figure 5
<p>Carrier Loop Design Structure.</p> Full article ">Figure 6
<p>(<b>a</b>) Phase Detection Result of BOC(1,1). (<b>b</b>) Phase Detection Result of BOC(6,1). (<b>c</b>) Phase Detection Result of QMBOC(6,1,4/33).</p> Full article ">Figure 7
<p>Multi-correlation Peak Phase Deblurring Algorithm Structure.</p> Full article ">Figure 8
<p>(<b>a</b>) Cross-correlation Values in the Subcarrier Domain. (<b>b</b>) Phase Detection Values in the Subcarrier Domain. (<b>c</b>) Cross-correlation Values in the Pseudocode Domain. (<b>d</b>) Phase Detection Values in the Pseudocode Domain.</p> Full article ">Figure 9
<p>(<b>a</b>) Comparison of Pseudocode and Subcarrier Phase Detection. (<b>b</b>) Phase Detection for Different Subcarriers.</p> Full article ">Figure 10
<p>The Steps of the Code Phase Deblurring Algorithm.</p> Full article ">Figure 11
<p>(<b>a</b>) Comparative Analysis of Code Correction Parameters in QMBOC Signals. (<b>b</b>) Comparative Analysis of Tracking Methods for QMBOC Signals.</p> Full article ">Figure 12
<p>Comparative Analysis of In-Phase (I) and Quadrature (Q) Value Distributions for Separate Tracking of B1C Pilot and Data Components. (<b>a</b>) Noise-free pilot branch. (<b>b</b>) Noise-free data branch. (<b>c</b>) SNR = −20 dB pilot branch. (<b>d</b>) SNR = −20 dB data branch.</p> Full article ">Figure 13
<p>Comparative Analysis of Doppler Estimation for Separate Tracking of B1C Pilot and Data Components. (<b>a</b>) Noise-free pilot branch. (<b>b</b>) Noise-free data branch. (<b>c</b>) SNR = −20 dB pilot branch. (<b>d</b>) SNR = −20 dB data branch.</p> Full article ">Figure 14
<p>Comparative Analysis of Code Phase Estimation for Separate Tracking of B1C Pilot and Data Components. (<b>a</b>) Noise-free pilot branch. (<b>b</b>) Noise-free data branch. (<b>c</b>) SNR = −20 dB pilot branch. (<b>d</b>) SNR = −20 dB data branch.</p> Full article ">Figure 15
<p>Comparison of Subcarrier Phase Error Under Different Chip Intervals. (<b>a</b>) Comparison of subcarrier intervals for BOC(1,1). (<b>b</b>) Comparison of subcarrier intervals for BOC(6,1).</p> Full article ">Figure 16
<p>Carrier Loop Tracking Results.</p> Full article ">Figure 17
<p>Pseudocode Loop Tracking Results.</p> Full article ">Figure 18
<p>Comparison of Root Mean Square Error.</p> Full article ">Figure 19
<p>Testing Hardware Platform Design.</p> Full article ">Figure 20
<p>(<b>a</b>) Testing Hardware Platform. (<b>b</b>) Test Route.</p> Full article ">Figure 21
<p>The tracking status of the loop when passing through obstructed areas. (<b>a</b>) Overview. (<b>b</b>) Details.</p> Full article ">Figure 22
<p>(<b>a</b>) The instantaneous states of the FLL and PLL in the receiver’s carrier loop before and after stable signal tracking. (<b>b</b>) FLL Details. (<b>c</b>) PLL Details. (<b>d</b>) The instantaneous states of the prompt I branch and prompt Q branch in the receiver’s tracking loop before and after achieving stability. (<b>e</b>) I branch Details. (<b>f</b>) Q branch Details.</p> Full article ">
29 pages, 23352 KiB  
Article
GNSS-Based Driver Assistance for Charging Electric City Buses: Implementation and Lessons Learned from Field Testing
by Iman Esfandiyar, Krzysztof Ćwian, Michał R. Nowicki and Piotr Skrzypczyński
Remote Sens. 2023, 15(11), 2938; https://doi.org/10.3390/rs15112938 - 5 Jun 2023
Cited by 1 | Viewed by 1868
Abstract
Modern public transportation in urban areas increasingly relies on high-capacity buses. At the same time, the share of electric vehicles is increasing to meet environmental standards. This introduces problems when charging these vehicles from chargers at bus stops, as untrained drivers often find [...] Read more.
Modern public transportation in urban areas increasingly relies on high-capacity buses. At the same time, the share of electric vehicles is increasing to meet environmental standards. This introduces problems when charging these vehicles from chargers at bus stops, as untrained drivers often find it difficult to execute docking manoeuvres on the charger. A practical solution to this problem requires a suitable advanced driver-assistance system (ADAS), which is a system used to automatise and make safer some of the tasks involved in driving a vehicle. In the considered case, ADAS supports docking to the electric charging station, and thus, it must solve two issues: precise positioning of the bus relative to the charger and motion planning in a constrained space. This paper addresses these issues by employing GNSS-based positioning and optimisation-based planning, resulting in an affordable solution to the ADAS for the docking of electric buses while recharging. We focus on the practical side of the system, showing how the necessary features were attained at a limited hardware and installation cost, also demonstrating an extensive evaluation of the fielded ADAS for an operator of public transportation in the city of Poznań in Poland. Full article
(This article belongs to the Special Issue GNSS for Urban Transport Applications II)
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Figure 1

Figure 1
<p>Hardware used in the ADAS system. The onboard computer performs all the calculations, the GNSS modules provide the position and heading of the bus, the CAN-USB adapter enables reading data from the sensors of a bus, the LTE module receives RTCM corrections over the network from the base station, and the LCD presents the system interface to the driver.</p> Full article ">Figure 2
<p>Mounting positions of the GNSS antennas with respect to the geometry of a bus roof. The first antenna, connected to the moving base receiver, is mounted <math display="inline"><semantics> <mrow> <mn>0.2</mn> </mrow> </semantics></math> m ahead of the rear axle and provides the position of a vehicle. The second one, connected to the rover module, is mounted 5 m in front of the first one and is used to calculate the bus heading.</p> Full article ">Figure 3
<p>The architecture of the developed system. The software components were implemented as the following ROS nodes: NTRIP client, GNSS driver, CAN bridge, localisation node, system activation node, path planner, and feedback controller. The NTRIP client receives the RTCM correction using the LTE module, the GNSS driver handles the data coming from u-blox F9P receivers, the CAN bridge provides the interface for communication with the sensors mounted in the bus, the localisation node calculates the pose of a bus in relation to a charging station, the system activation node launches the motion-planning system when the bus approaches the depot, and the path planner and feedback controller generate the reference path for the driver and guide him during the docking manoeuvre.</p> Full article ">Figure 4
<p>The recorded routes of the city bus plotted on the <span class="html-italic">OpenStreetMap</span>. RTK FIXED poses are marked with the blue line, less accurate RTK FLOAT poses are marked with a red line, and the least accurate standard GNSS poses are marked with the yellow line. Plot (<b>A</b>) shows the map for Dworzec Zachodni–Kacza, (<b>B</b>) for Garbary PKM–Strzeszyn, and (<b>C</b>) for Garbary PKM–Os. Dębina route.</p> Full article ">Figure 5
<p>Exemplary parts of the trajectories, where the RTK FIXED mode of GNSS was not available because of the poor sky visibility caused by buildings and trees. Image (<b>1</b>) presents the part of the trajectory from <a href="#remotesensing-15-02938-f004" class="html-fig">Figure 4</a>B, while image (<b>2</b>) shows an example scene from <a href="#remotesensing-15-02938-f004" class="html-fig">Figure 4</a>C.</p> Full article ">Figure 6
<p>Absolute Trajectory Error calculated between the <span class="html-italic">moving_base</span> (reference) and <span class="html-italic">rover</span> positions. The error was calculated for the part of the Garbary PKM–Os. Dębina route where the RTK FIXED mode of GNSS was partially not available due to high buildings and trees located near the road.</p> Full article ">Figure 7
<p>Position of chargers at the Garbary PKM depot (<b>A</b>). The green marker indicates the charger for which the developed system was set up. The GNSS localisation mode around the bus depot (<b>B</b>), including the approach path for which the most accurate RTK FIXED mode was available. The arrows indicate the driving direction.</p> Full article ">Figure 8
<p>The electric urban bus, Solaris Urbino 12 Electric, used for the experiment in this work.</p> Full article ">Figure 9
<p>Car-like robot kinematics used to describe the electric bus.</p> Full article ">Figure 10
<p>Bounding boxes are generated to provide the planner with a safe and obstacle-free tunnel interpreted as positional inequality containers; in this figure, (<b>A</b>–<b>C</b>) present different locations subject to path planning. The blue arrow denotes the initial configuration of the bus while the green one charger configuration.</p> Full article ">Figure 11
<p>Block scheme explaining connections between the control module and the interface subsystem.</p> Full article ">Figure 12
<p>Human–machineinterface in ADAS system. The top purple coloured bar indicates the path error: that is, the displacement of the bus’s guidance point from the reference path. The main bar provides steering guidance, consisting of a solid blue line visualising the desired steering angle, and a coloured-filled area indicating the actual steering angle. The colour of the bar is sensitive to the value of the steering error: an acceptable error is shown by green colour, while the bigger the error becomes, the colour changes to orange and red. The vertical bar placed on the right side of the window indicates the remaining distance to the charger; at zero, the pantograph is located exactly below the charger. (<b>A</b>): At this state, the centre of the rear axis of the bus (guidance point) is more than one metre away from the reference path, and the driver must turn the steering wheel to the right to align the actual steering with the blue line indicating the desired steering angle. The red portion of the right vertical bar shows the bus still has to proceed further to reach the charger. (<b>B</b>): The actual and reference steering is aligned with acceptable precision; however, the guidance point is still more than one metre away from the reference path. From the vertical bar, it can be seen that the bus is closer to the goal pose compared to the image on the left. (<b>C</b>): At this state, the reference steering angle is shown around zero, and the actual steering angle is shown around the same value. As the driver follows the guidance and keeps the wheels straight, the bus remains on the reference path and finally reaches the goal pose.</p> Full article ">Figure 13
<p>Reference and real path of the bus generated for the bus approach to the charging station. The point (0, 0) is the charger location (<b>A</b>). The Absolute Trajectory Error between the reference and real trajectory over time (<b>B</b>). Plots present the results for sequence 42.</p> Full article ">Figure 14
<p>Reference and real path of the bus, generated for the bus approach to the charging station. The point (0, 0) is the charger location (<b>A</b>). The Absolute Trajectory Error between the reference and real trajectory over time (<b>B</b>). Plots present the results for sequence 12.</p> Full article ">Figure 15
<p>The average deviation between the planned and actual path in relation to the distance from the charger. The average value was calculated based on all 50 sequences.</p> Full article ">Figure 16
<p>The visualisation of the docking manoeuvre trajectory for the case when the driver approaches a different charger than assumed by our system.</p> Full article ">
27 pages, 5349 KiB  
Article
High-Accuracy Absolute-Position-Aided Code Phase Tracking Based on RTK/INS Deep Integration in Challenging Static Scenarios
by Yiran Luo, Li-Ta Hsu, Yang Jiang, Baoyu Liu, Zhetao Zhang, Yan Xiang and Naser El-Sheimy
Remote Sens. 2023, 15(4), 1114; https://doi.org/10.3390/rs15041114 - 17 Feb 2023
Cited by 1 | Viewed by 2524
Abstract
Many multi-sensor navigation systems urgently demand accurate positioning initialization from global navigation satellite systems (GNSSs) in challenging static scenarios. However, ground blockages against line-of-sight (LOS) signal reception make it difficult for GNSS users. Steering local codes in GNSS basebands is a desirable way [...] Read more.
Many multi-sensor navigation systems urgently demand accurate positioning initialization from global navigation satellite systems (GNSSs) in challenging static scenarios. However, ground blockages against line-of-sight (LOS) signal reception make it difficult for GNSS users. Steering local codes in GNSS basebands is a desirable way to correct instantaneous signal phase misalignment, efficiently gathering useful signal power and increasing positioning accuracy. Inertial navigation systems (INSs) have been used as effective complementary dead reckoning (DR) sensors for GNSS receivers in kinematic scenarios, resisting various forms of interference. However, little work has focused on whether INSs can improve GNSS receivers in static scenarios. Thus, this paper proposes an enhanced navigation system deeply integrated with low-cost INS solutions and GNSS high-accuracy carrier-based positioning. First, an absolute code phase is predicted from base station information and integrated solutions of the INS DR and real-time kinematic (RTK) results through an extended Kalman filter (EKF). Then, a numerically controlled oscillator (NCO) leverages the predicted code phase to improve the alignment between instantaneous local code phases and received ones. The proposed algorithm is realized in a vector-tracking GNSS software-defined radio (SDR). Results of the time-of-arrival (TOA) and positioning based on real-world experiments demonstrated the proposed SDR. Full article
(This article belongs to the Special Issue GNSS for Urban Transport Applications II)
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Figure 1
<p>Diagrammatic sketch reflecting the actual, and locally replicated, code signals varying with time regarding different algorithms, where colored curves correspond to the locally replicated code signals. (<b>a</b>) code phase misalignment is caused by the code frequency error and the initial (absolute) code phase error, light blue corresponds to the replicated signal based on STL (<b>b</b>) moderate frequency error is reduced, deep blue corresponds to the replicated signal based on standalone traditional VT (<b>c</b>) significant frequency error is reduced, light green corresponds to the replicated signal based on RTK/INS traditional VT (<b>d</b>) significant frequency error and moderate initial code phase error are reduced, deep green corresponds to replicated signal based on RTK VDFPLL (<b>e</b>) significant frequency error and significant initial code phase error are reduced, pink corresponds to replicated signal based on proposed RTK/INS VDFPLL. Dashed red lines correspond to timestamp.</p> Full article ">Figure 2
<p>Comparison of the instantaneous code phase error at the timestamp (see the dashed red line in <a href="#remotesensing-15-01114-f001" class="html-fig">Figure 1</a>) for extracting the instantaneous GNSS measurements (e.g., pseudoranges and carrier phases). It is worth mentioning that the code phase errors are exaggerated in this diagram by omitting random noise and raising the biased errors to show the differences in algorithms intuitively. The actual code phase errors usually do not exceed half of the early–late chip spacing. The numerical values in the figure correspond to the code phase.</p> Full article ">Figure 3
<p>Overview of the GNSS baseband architecture with the RTK-position-aided VDFPLL [<a href="#B26-remotesensing-15-01114" class="html-bibr">26</a>,<a href="#B30-remotesensing-15-01114" class="html-bibr">30</a>].</p> Full article ">Figure 4
<p>Architecture of the proposed VDFPLL-enhanced GPS SDR based on the deep integration of float RTK solutions and INS DR navigation results (detailed discussions refer to the Algorithm 1).</p> Full article ">Figure 5
<p>Setup for the stationary experiments.</p> Full article ">Figure 6
<p>Open-sky test spot (Google Map show) and sky plot of available GPS satellites.</p> Full article ">Figure 7
<p>Single point navigation results and statistical analysis of different SDRs in the open-sky situation where dashed lines correspond to outlier epochs. (<b>a</b>) DOP values (<b>b</b>) SPP results (<b>c</b>) CDF curves of 3D SPP RMSE.</p> Full article ">Figure 8
<p>RTK position results and statistical analysis of different SDRs in the open-sky situation where dashed lines correspond to the outlier epochs. (<b>a</b>) RTK position errors (<b>b</b>) horizontal RTK results in Google Map (<b>c</b>) CDF curves of 3D RTK RMSE (<b>d</b>) CDF curves of horizontal (2D) RTK RMSE.</p> Full article ">Figure 9
<p>RTK position results and statistical analysis of different SDRs in the open-sky situation where dashed lines correspond to the outlier epochs. (<b>a</b>) RTK position errors (<b>b</b>) horizontal RTK results in Google Map (<b>c</b>) CDF curves of horizontal (2D) RTK RMSE (<b>d</b>) CDF curves of 3D RTK RMSE.</p> Full article ">Figure 10
<p>Error curves of the TOA residuals for the GPS satellites PRN1 and PRN22 in the open-sky situation.</p> Full article ">Figure 11
<p>Semi-open-sky test spot (Google Map show) and the sky plot of available GPS satellites.</p> Full article ">Figure 12
<p>Single point navigation results and statistical analysis of different SDRs in the semi-open-sky situation where dashed lines correspond to the outlier epochs. (<b>a</b>) DOP values (<b>b</b>) SPP results (<b>c</b>) CDF curves of 3D SPP RMSE.</p> Full article ">Figure 13
<p>RTK position results and statistical analysis of different SDRs in the semi-open-sky situation where dashed lines correspond to the outlier epochs. (<b>a</b>) RTK position error (<b>b</b>) horizontal RTK position results in Google Map (<b>c</b>) CDF curves of 3D RTK RMSE.</p> Full article ">Figure 14
<p>RTK/INS integration position results and statistical analysis of different SDRs where dashed lines correspond to the outlier epochs in the semi-open-sky situation. (<b>a</b>) RTK/INS integration position error (<b>b</b>) horizontal RTK/INS integration position results in Google Map (<b>c</b>) CDF curves of 2D RTK/INS integration position RMSE (<b>d</b>) CDF curves of 3D RTK/INS integration position RMSE.</p> Full article ">Figure 15
<p>Error curves of the TOA residuals for the GPS satellites PRN6 and PRN3 in the semi-open-sky situation.</p> Full article ">Figure 16
<p>TOA curve references (regarding the error curves of the TOA residuals) derived from the ground-truth-based VDFPLL SDR. (<b>left</b>) TOA reference from PRN22 for the open-sky experiment (<b>right</b>) TOA reference from PRN3 for the semi-open-sky experiment.</p> Full article ">Figure 17
<p>Comparison of TOA accuracy improvements varying with the satellite elevation angles and the TOA errors (positive and negative values represent the improved and reduced performance percentages, respectively).</p> Full article ">Figure 18
<p>APA code phase errors from the proposed RTK/INS-based VDFPLL SDR where the numbers correspond to the satellite PRN numbers and dashed black lines correspond to the estimates from the traditional scalar and vector tracking algorithms (<b>a</b>) open sky (<b>b</b>) semi-open sky.</p> Full article ">
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