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High-Precision and High-Reliability Positioning, Navigation, and Timing: Opportunities and Challenges

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

Deadline for manuscript submissions: 19 July 2024 | Viewed by 9069

Special Issue Editors


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School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China
Interests: precise positioning and navigation with GNSS and multi-sensor systems under complex conditions including challenging environments; low-cost devices; multi-source data
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Guest Editor
Institute of Geospatial Information, Information Engineering University, Zhengzhou 450000, China
Interests: multi-frequency and multi-constellation GNSS precise positioning; multi-sensor integrated precise positioning
College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China
Interests: GNSS high-precision positioning technologies; underwater positioning and navigation

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Guest Editor

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Guest Editor

Special Issue Information

Dear Colleagues,

Global Navigation Satellite Systems (GNSS) provide high-precision positioning, navigation, and timing (PNT) capabilities in open areas and have gained widespread use in various fields, including high-precision monitoring and intelligent transportation. However, their performance is hindered in challenging environments where signals are susceptible to reflection, refraction, diffraction, and blockage by buildings. These factors can result in unreliable or intermittent PNT with GNSS. Fortunately, certain sensors complement GNSS and thus multi-source sensors, including the inertial measurement unit (IMU), light detection and ranging (LiDAR), and vision and odometer sensors, are extensively explored and employed, particularly in autonomous driving and ground unmanned vehicles. Simultaneous Localization and Mapping (SLAM) stands out as a notable application of multi-source sensor fusion, attracting significant attention due to its high robustness and accuracy. The diversification of GNSS systems and constellations, multi-source sensors, and observation environments put forward higher requirements for technology and algorithms to maintain high-precision and high-reliability PNT services. Advanced algorithms serve as the key to solving practical application issues related to GNSS and multi-source sensors, thereby expanding their scope of applications.

This Special Issue aims at studies covering improved methods and the latest challenges in PNT, especially in challenging environments for various research investigations as well as a range of practical applications. We strongly encourage both theoretical and applied research contributions on GNSS or multi-source sensor fusion technology in all disciplines. Topics may cover anything from high-precision and high-reliability PNT with GNSS or multi-source sensors, resilient PNT with GNSS or multi-source sensors in challenging environments, integrated PNT with GNSS and multi-sensor systems, and applications of PNT with GNSS or multi-source sensors. Therefore, new algorithms for high-precision positioning and navigation, fusion of multi-sensor systems, software development for data collection, integration, and processing, and their applications in various fields are welcome.

Articles may address, but are not limited to, the following topics:

  • High-precision/High-reliability PNT with GNSS.
  • High-precision/High-reliability PNT with multi-source sensors.
  • Resilient PNT with GNSS in challenging environments.
  • Resilient PNT with multi-source sensors in challenging environments.
  • Integrated PNT with GNSS and multi-sensor systems.
  • Applications of PNT with GNSS/multi-source sensors.

Dr. Zhetao Zhang
Dr. Guorui Xiao
Dr. Zhixi Nie
Prof. Dr. Vagner Ferreira
Dr. Giuseppe Casula
Guest Editors

Manuscript Submission Information

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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

  • positioning navigation and timing
  • GNSS
  • multi-sensor system
  • challenging environment
  • applications of GNSS and multi-sensor systems

Published Papers (9 papers)

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17 pages, 3795 KiB  
Article
Regional Real-Time between-Satellite Single-Differenced Ionospheric Model Establishing by Multi-GNSS Single-Frequency Observations: Performance Evaluation and PPP Augmentation
by Ahao Wang, Yize Zhang, Junping Chen, Xuexi Liu and Hu Wang
Remote Sens. 2024, 16(9), 1511; https://doi.org/10.3390/rs16091511 - 25 Apr 2024
Viewed by 339
Abstract
The multi-global navigation satellite system (GNSS) undifferenced and uncombined precise point positioning (UU-PPP), as a high-precision ionospheric observables extraction technology superior to the traditional carrier-to-code leveling (CCL) method, has received increasing attention. In previous research, only dual-frequency (DF) or multi-frequency (MF) observations are [...] Read more.
The multi-global navigation satellite system (GNSS) undifferenced and uncombined precise point positioning (UU-PPP), as a high-precision ionospheric observables extraction technology superior to the traditional carrier-to-code leveling (CCL) method, has received increasing attention. In previous research, only dual-frequency (DF) or multi-frequency (MF) observations are used to extract slant ionospheric delay with the UU-PPP. To reduce the cost of ionospheric modeling, the feasibility of extracting ionospheric observables from the multi-GNSS single-frequency (SF) UU-PPP was investigated in this study. Meanwhile, the between-satellite single-differenced (SD) method was applied to remove the effects of the receiver differential code bias (DCB) with short-term time-varying characteristics in regional ionospheric modeling. In the assessment of the regional real-time (RT) between-satellite SD ionospheric model, the internal accord accuracy of the SD ionospheric delay can be better than 0.5 TECU, and its external accord accuracy within 1.0 TECU is significantly superior to three global RT ionospheric models. With the introduction of the proposed SD ionospheric model into the multi-GNSS kinematic RT SF-PPP, the initialization speed of vertical positioning errors can be improved by 21.3% in comparison with the GRAPHIC (GRoup And PHase Ionospheric Correction) SF-PPP model. After reinitialization, both horizontal and vertical positioning errors of the SD ionospheric constrained (IC) SF-PPP can be maintained within 0.2 m. This proves that the proposed SDIC SF-PPP model can enhance the continuity and stability of kinematic positioning in the case of some GNSS signals missing or blocked. Compared with the GRAPHIC SF-PPP, the horizontal positioning accuracy of the SDIC SF-PPP in kinematic mode can be improved by 37.9%, but its vertical positioning accuracy may be decreased. Overall, the 3D positioning accuracy of the SD ionospheric-constrained RT SF-PPP can be better than 0.3 m. Full article
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17 pages, 6856 KiB  
Article
Stationary Detection for Zero Velocity Update of IMU Based on the Vibrational FFT Feature of Land Vehicle
by Mowen Li, Wenfeng Nie, Vladimir Suvorkin, Adria Rovira-Garcia, Wei Zhang, Tianhe Xu and Guochang Xu
Remote Sens. 2024, 16(5), 902; https://doi.org/10.3390/rs16050902 - 4 Mar 2024
Viewed by 721
Abstract
The inertial navigation system (INS) and global satellite navigation system (GNSS) are two of the most significant systems for land navigation applications. The inertial measurement unit (IMU) is a kind of INS sensor that measures three-dimensional acceleration and angular velocity measurements. IMUs based [...] Read more.
The inertial navigation system (INS) and global satellite navigation system (GNSS) are two of the most significant systems for land navigation applications. The inertial measurement unit (IMU) is a kind of INS sensor that measures three-dimensional acceleration and angular velocity measurements. IMUs based on micro-electromechanical systems (MEMSs) are widely employed in vehicular navigation thanks to their low cost and small size, but their magnitude and noisy biases make navigation errors diverge very fast without external constraint. The zero-velocity update (ZVU) function is one of the efficient functions that constrain the divergence of IMUs for a stopped vehicle, and the key of the ZVU is the correct stationary detection for the vehicle. When a land vehicle is stopped, the idling engine produces a very stable vibration, which allows us to perform frequency analysis and a comparison based on the fast Fourier transform (FFT) and IMU measurements. Hence, we propose a stationary detection method based on the FFT for a stopped land vehicle with an idling engine in this study. An urban vehicular navigation experiment was carried out with our GNSS/IMU integration platform. Three stops for 10 to 20 min were set to analyze, generate and evaluate the FFT-based stationary detection method. The FFT spectra showed clearly idling vibrational peaks during the three stop periods. Through the comparison of FFT spectral features with decelerating and accelerating periods, the amplitudes of vibrational peaks were put forward as the key factors of stationary detection. For the consecutive stationary detection in the GNSS/IMU integration process, a three-second sliding window with a one-second updating rate of the FFT was applied to check the amplitudes of peaks. For the assessment of the proposed stationary detection method, GNSS observations were removed to simulate outages during the three stop periods, and the proposed detection method was conducted together with the ZVU. The results showed that the proposed method achieved a 99.7% correct detection rate, and the divergence of the positioning error constrained via the ZVU was within 2 cm for the experimental stop periods, which indicates the effectiveness of the proposed method. Full article
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23 pages, 6995 KiB  
Article
A Robust Position Estimation Method in the Integrated Navigation System via Factor Graph
by Sihang Quan, Shaohua Chen, Yilan Zhou, Shuai Zhao, Huizhu Hu and Qi Zhu
Remote Sens. 2024, 16(3), 562; https://doi.org/10.3390/rs16030562 - 31 Jan 2024
Viewed by 1047
Abstract
Achieving higher accuracy and robustness stands as the central objective in the navigation field. In complex urban environments, the integrity of GNSS faces huge challenges and the performance of integrated navigation systems can be significantly affected. As the proportion of faulty measurements rises, [...] Read more.
Achieving higher accuracy and robustness stands as the central objective in the navigation field. In complex urban environments, the integrity of GNSS faces huge challenges and the performance of integrated navigation systems can be significantly affected. As the proportion of faulty measurements rises, it can result in both missed alarms and false positives. In this paper, a robust method based on factor graph is proposed to improve the performance of integrated navigation systems. We propose a detection method based on multi-conditional analysis to determine whether GNSS is anomalous or not. Moreover, the optimal weight of GNSS measurement is estimated under anomalous conditions to mitigate the impact of GNSS outliers. The proposed method is evaluated through real-world road tests, and the results show the positioning accuracy of the proposed method is improved by more than 60% and the missed alarm rate is reduced by 80% compared with the traditional algorithms. Full article
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23 pages, 9588 KiB  
Article
DLD-SLAM: RGB-D Visual Simultaneous Localisation and Mapping in Indoor Dynamic Environments Based on Deep Learning
by Han Yu, Qing Wang, Chao Yan, Youyang Feng, Yang Sun and Lu Li
Remote Sens. 2024, 16(2), 246; https://doi.org/10.3390/rs16020246 - 8 Jan 2024
Cited by 1 | Viewed by 1179
Abstract
This work presents a novel RGB-D dynamic Simultaneous Localisation and Mapping (SLAM) method that improves the precision, stability, and efficiency of localisation while relying on lightweight deep learning in a dynamic environment compared to the traditional static feature-based visual SLAM algorithm. Based on [...] Read more.
This work presents a novel RGB-D dynamic Simultaneous Localisation and Mapping (SLAM) method that improves the precision, stability, and efficiency of localisation while relying on lightweight deep learning in a dynamic environment compared to the traditional static feature-based visual SLAM algorithm. Based on ORB-SLAM3, the GCNv2-tiny network instead of the ORB method, improves the reliability of feature extraction and matching and the accuracy of position estimation; then, the semantic segmentation thread employs the lightweight YOLOv5s object detection algorithm based on the GSConv network combined with a depth image to determine potentially dynamic regions of the image. Finally, to guarantee that the static feature points are used for position estimation, dynamic probability is employed to determine the true dynamic feature points based on the optical flow, semantic labels, and the state in last frame. We have performed experiments on the TUM datasets to verify the feasibility of the algorithm. Compared with the classical dynamic visual SLAM algorithm, the experimental results demonstrate that the absolute trajectory error is greatly reduced in dynamic environments, and that the computing efficiency is improved by 31.54% compared with the real-time dynamic visual SLAM algorithm with close accuracy, demonstrating the superiority of DLD-SLAM in accuracy, stability, and efficiency. Full article
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21 pages, 8116 KiB  
Article
Characterization of BDS Multipath Effect Based on AT-Conv-LSTM Network
by Jie Sun, Zuping Tang, Chuang Zhou and Jiaolong Wei
Remote Sens. 2024, 16(1), 73; https://doi.org/10.3390/rs16010073 - 24 Dec 2023
Viewed by 740
Abstract
Multipath effects are the most challenging error sources for the Global Navigation Satellite System receiver, affecting observation quality and positioning accuracy. Due to the non-linear and time-varying nature, multipath error is difficult to process. Previous studies used a homogeneous indicator to characterize multipath [...] Read more.
Multipath effects are the most challenging error sources for the Global Navigation Satellite System receiver, affecting observation quality and positioning accuracy. Due to the non-linear and time-varying nature, multipath error is difficult to process. Previous studies used a homogeneous indicator to characterize multipath effects and only revealed the temporal or spatial correlations of the multipath, resulting in limited correction performance. In this study, we consider the code multipath to be influenced not only by the elevation and azimuth angle of certain stations to satellites but also to be related to satellite characteristics such as nadir angle. Hence, azimuth angle, elevation angle, nadir angle and carrier-to-noise power density ratio are taken as multiple indicators to characterize the multipath significantly. Then, we propose an Attention-based Convolutional Long Short-Term Memory (AT-Conv-LSTM) that fully exploits the spatiotemporal correlations of multipath derived from multiple indicators. The main processing procedures using AT-Conv-LSTM are given. Finally, the AT-Conv-LSTM is applied to a station for 16 consecutive days to verify the multipath mitigation effectiveness. Compared with sidereal filtering, multipath hemispherical map (MHM) and trend-surface analysis-based MHM, the experimental results show that using AT-Conv-LSTM can decrease the root mean square error and mean absolute error values of the multipath error more than 60% and 13%, respectively. The proposed method can correct the code multipath to centimeter level, which is one order of magnitude lower than the uncorrected code multipath. Therefore, the proposed AT-Conv-LSTM network could be used as a powerful alternative tool to realize multipath reduction and will be of wide practical value in the fields of standard and high-precision positioning services. Full article
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21 pages, 4877 KiB  
Article
A Low-Cost and Robust Multi-Sensor Data Fusion Scheme for Heterogeneous Multi-Robot Cooperative Positioning in Indoor Environments
by Zhi Cai, Jiahang Liu, Weijian Chi and Bo Zhang
Remote Sens. 2023, 15(23), 5584; https://doi.org/10.3390/rs15235584 - 30 Nov 2023
Cited by 2 | Viewed by 1119
Abstract
The latest development of multi-robot collaborative systems has put forward higher requirements for multi-sensor fusion localization. Current position methods mainly focus on the fusion of the carrier’s own sensor information, and how to fully utilize the information of multiple robots to achieve high-precision [...] Read more.
The latest development of multi-robot collaborative systems has put forward higher requirements for multi-sensor fusion localization. Current position methods mainly focus on the fusion of the carrier’s own sensor information, and how to fully utilize the information of multiple robots to achieve high-precision positioning is a major challenge. However, due to the comprehensive impact of factors such as poor performance, variety, complex calculations, and accumulation of environmental errors used by commercial robots, the difficulty of high-precision collaborative positioning is further exacerbated. To address this challenge, we propose a low-cost and robust multi-sensor data fusion scheme for heterogeneous multi-robot collaborative navigation in indoor environments, which integrates data from inertial measurement units (IMUs), laser rangefinders, cameras, and so on, into heterogeneous multi-robot navigation. Based on Discrete Kalman Filter (DKF) and Extended Kalman Filter (EKF) principles, a three-step joint filtering model is used to improve the state estimation and the visual data are processed using the YOLO deep learning target detection algorithm before updating the integrated filter. The proposed integration is tested at multiple levels in an open indoor environment following various formation paths. The results show that the three-dimensional root mean square error (RMSE) of indoor cooperative localization is 11.3 mm, the maximum error is less than 21.4 mm, and the motion error in occluded environments is suppressed. The proposed fusion scheme is able to satisfy the localization accuracy requirements for efficient and coordinated motion of autonomous mobile robots. Full article
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13 pages, 7379 KiB  
Communication
Advancing Ultra-High Precision in Satellite–Ground Time–Frequency Comparison: Ground-Based Experiment and Simulation Verification for the China Space Station
by Yanming Guo, Shuaihe Gao, Zhibing Pan, Pei Wang, Xuewen Gong, Jiangyu Chen, Kun Song, Zhen Zhong, Yaoli Yue, Lishu Guo, Yan Bai, Yuping Gao, Xiaochun Lu and Shougang Zhang
Remote Sens. 2023, 15(22), 5393; https://doi.org/10.3390/rs15225393 - 17 Nov 2023
Viewed by 875
Abstract
Establishing an ultra-high-precision link for time–frequency comparisons between satellites and ground stations is critically important. This endeavor is fundamental to the advancement of pioneering space science exploration and the development of a robust space-based time–frequency system featuring ultra-high-precision space atomic clocks. In response [...] Read more.
Establishing an ultra-high-precision link for time–frequency comparisons between satellites and ground stations is critically important. This endeavor is fundamental to the advancement of pioneering space science exploration and the development of a robust space-based time–frequency system featuring ultra-high-precision space atomic clocks. In response to the requirements for assessing the long-term stability of high-precision space atomic clocks, we have designed and implemented a satellite–ground microwave time–frequency comparison system and method based on a three-frequency mode. Ground-based experimental results demonstrate that the equipment layer can achieve a satellite–ground time comparison accuracy better than 0.4 ps (RMS), with the equipment delay stability (ADEV) for all three frequencies being better than 8 × 10−18 at 86,400. By leveraging the ground-based experimental results, we constructed a satellite–ground time–frequency comparison simulation and verification platform. This platform realizes ultra-high-precision satellite–ground time–frequency comparison based on the China Space Station (CSS). After correcting various transmission delay errors, the satellite–ground time comparison achieved an accuracy better than 0.8 ps and an ADEV better than 2 × 10−17 at 86,400. This validation of our novel satellite–ground time–frequency comparison system and method, capable of achieving an 10−17 magnitude stability, is not only a significant contribution to the field of space time–frequency systems but also paves the way for future advancements and applications in space science exploration. Full article
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19 pages, 14133 KiB  
Article
An Improved Carrier-Smoothing Code Algorithm for BDS Satellites with SICB
by Qichao Zhang, Xiaping Ma, Yuting Gao, Gongwen Huang and Qingzhi Zhao
Remote Sens. 2023, 15(21), 5253; https://doi.org/10.3390/rs15215253 - 6 Nov 2023
Viewed by 869
Abstract
Carrier Smoothing Code (CSC), as a low-pass filter, has been widely used in GNSS positioning processing to reduce pseudorange noise via carrier phases. However, current CSC methods do not consider the systematic bias between the code and carrier phase observation, also known as [...] Read more.
Carrier Smoothing Code (CSC), as a low-pass filter, has been widely used in GNSS positioning processing to reduce pseudorange noise via carrier phases. However, current CSC methods do not consider the systematic bias between the code and carrier phase observation, also known as Satellite-induced Code Bias (SICB). SICB has been identified in the BDS-2 and the bias will reduce the accuracy or reliability of the CSC. To confront bias, an improved CSC algorithm is proposed by considering SICB for GEO, IGSO, and MEO satellites in BDS constellations. The correction model of SICB for IGSO/MEO satellites is established by using a 0.1-degree interval piecewise weighted least squares Third-order Curve Fitting Method (TOCFM). The Variational Mode Decomposition combined with Wavelet Transform (VMD-WT) is proposed to establish the correction model of SICB for the GEO satellite. To verify the proposed method, the SICB model was established by collecting 30 Multi-GNSS Experiment (MGEX) BDS stations in different seasons of a year, in which the BDS data of ALIC, KRGG, KOUR, GCGO, GAMG, and SGOC stations were selected for 11 consecutive days to verify the effectiveness of the algorithm. The results show that there is obvious SICB in the BDS-2 Multipath (MP) combination, but the SICB in the BDS-3 MP is smaller and can be ignored. Compared with the modeling in the references, TOCFM is more suitable for IGSO/MEO SICB modeling, especially for the SICB correction at low elevation angles. After the VMD-WT correction, the Root Mean Square Error (RMSE) of SICB of B1I, B2I, and B3I in GEO satellites is reduced by 53.35%, 63.50%, and 64.71% respectively. Moreover, we carried out ionosphere-free Single Point Positioning (IF SPP), Ionosphere-free CSC SPP (IF CSC SPP), CSC single point positioning with the IGSO/MEO SICB Correction based on the TOCFA Method (IGSO/MEO SICB CSC), and CSC single point positioning with the IGSO/MEO/GEO SICB correction based on VMD-WT and TOCFA (IGSO/MEO/GEO SICB CSC), respectively. Compared to IF SPP, the average improvement of the IGSO/MEO/GEO SICB CSC algorithm in the north, east, and up directions was 24.42%, 27.94%, and 24.98%, respectively, and the average reduction in 3D RMSE is 24.54%. Compared with IF CSC SPP, the average improvement of IGSO/MEO/GEO SICB CSC is 7.03%, 6.50%, and 10.48% in the north, east, and up directions, respectively, while the average reduction in 3D RMSE was 9.86%. IGSO/MEO SICB mainly improves the U direction positioning accuracy, and GEO SICB mainly improves the E and U direction positioning accuracy. After the IGSO/MEO/GEO SICB correction, the overall improvement was about 10% and positioning improved to a certain extent. Full article
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14 pages, 2801 KiB  
Technical Note
Multi-Antenna Global Navigation Satellite System/Inertial Measurement Unit Tight Integration for Measuring Displacement and Vibration in Structural Health Monitoring
by Wujiao Dai, Xin Li, Wenkun Yu, Xuanyu Qu and Xiaoli Ding
Remote Sens. 2024, 16(6), 1072; https://doi.org/10.3390/rs16061072 - 18 Mar 2024
Viewed by 668
Abstract
Large-scale engineering structures deform and vibrate under the influence of external forces. Obtaining displacement and vibration is crucial for structural health monitoring (SHM). Global navigation satellite system (GNSS) and inertial measurement unit (IMU) are complementary and widely used in SHM. In this paper, [...] Read more.
Large-scale engineering structures deform and vibrate under the influence of external forces. Obtaining displacement and vibration is crucial for structural health monitoring (SHM). Global navigation satellite system (GNSS) and inertial measurement unit (IMU) are complementary and widely used in SHM. In this paper, we propose an SHM scheme where IMU and multi-antenna GNSS are tightly integrated. The phase centers of multiple GNSS antennas are transformed into the IMU center, which increases the observation redundancy and strengthens the positioning model. To evaluate the performance of tight integration of IMU and multiple GNSS antennas, high-rate vibrational signals are simulated using a shaking table, and the errors of horizontal displacement of different positioning schemes are analyzed using recordings of a high-precision ranging laser as the reference. The results demonstrate that applying triple-antenna GNSS/IMU integration for measuring the displacement can achieve an accuracy of 2.6 mm, which is about 33.0% and 30.3% superior than the accuracy achieved by the conventional single-antenna GNSS-only and GNSS/IMU solutions, respectively. Full article
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