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High-Precision and High-Reliability Positioning, Navigation, and Timing: Opportunities and Challenges
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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: closed (19 July 2024) | Viewed by 22041

Special Issue Editors

Dr. Zhetao Zhang

E-Mail Website
Guest Editor
School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China
Interests: GNSS precise positioning under complex conditions; canyon environment; low-cost receiver; multi-GNSS situation
Special Issues, Collections and Topics in MDPI journals
Dr. Guorui Xiao

E-Mail Website
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
Dr. Zhixi Nie

E-Mail Website
Guest Editor
College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China
Interests: GNSS high-precision positioning technologies; underwater positioning and navigation
Prof. Dr. Vagner Ferreira

E-Mail Website
Guest Editor
School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China
Interests: geodetic sensors for hydrology; earth system; groundwater; climate change
Special Issues, Collections and Topics in MDPI journals
Dr. Giuseppe Casula

E-Mail Website
Guest Editor
Istituto Nazionale di Geofisica e Vulcanologia (INGV)—Sezione di Bologna, Viale Berti Pichat 6/2, 40127 Bologna, Italy
Interests: geodesy; terrestrial laser scanner (TLS); structure from motion (SfM); close-range photogrammetry (CRP); GNSS; DinSAR; gravimetry
Special Issues, Collections and Topics in MDPI journals

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

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Published Papers (16 papers)

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19 pages, 15208 KiB  
Article
Analysis of the Influence of Refraction-Parameter Deviation on Underwater Stereo-Vision Measurement with Flat Refraction Interface
by Guanqing Li, Shengxiang Huang, Zhi Yin, Nanshan Zheng and Kefei Zhang
Remote Sens. 2024, 16(17), 3286; https://doi.org/10.3390/rs16173286 - 4 Sep 2024
Viewed by 586
Abstract
There has been substantial research on multi-medium visual measurement in fields such as underwater three-dimensional reconstruction and underwater structure monitoring. Addressing the issue where traditional air-based visual-measurement models fail due to refraction when light passes through different media, numerous studies have established refraction-imaging [...] Read more.
There has been substantial research on multi-medium visual measurement in fields such as underwater three-dimensional reconstruction and underwater structure monitoring. Addressing the issue where traditional air-based visual-measurement models fail due to refraction when light passes through different media, numerous studies have established refraction-imaging models based on the actual geometry of light refraction to compensate for the effects of refraction on cross-media imaging. However, the calibration of refraction parameters inevitably contains errors, leading to deviations in these parameters. To analyze the impact of refraction-parameter deviations on measurements in underwater structure visual navigation, this paper develops a dual-media stereo-vision measurement simulation model and conducts comprehensive simulation experiments. The results indicate that to achieve high-precision underwater-measurement outcomes, the calibration method for refraction parameters, the distribution of the targets in the field of view, and the distance of the target from the camera must all be meticulously designed. These findings provide guidance for the construction of underwater stereo-vision measurement systems, the calibration of refraction parameters, underwater experiments, and practical applications. Full article
(This article belongs to the Special Issue High-Precision and High-Reliability Positioning, Navigation, and Timing: Opportunities and Challenges)
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22 pages, 18614 KiB  
Article
Visual Localization Method for Unmanned Aerial Vehicles in Urban Scenes Based on Shape and Spatial Relationship Matching of Buildings
by Yu Liu, Jing Bai and Fangde Sun
Remote Sens. 2024, 16(16), 3065; https://doi.org/10.3390/rs16163065 - 20 Aug 2024
Viewed by 574
Abstract
In urban scenes, buildings are usually dense and exhibit similar shapes. Thus, existing autonomous unmanned aerial vehicle (UAV) localization schemes based on map matching, especially the semantic shape matching (SSM) method, cannot capture the uniqueness of buildings and may result in matching failure. [...] Read more.
In urban scenes, buildings are usually dense and exhibit similar shapes. Thus, existing autonomous unmanned aerial vehicle (UAV) localization schemes based on map matching, especially the semantic shape matching (SSM) method, cannot capture the uniqueness of buildings and may result in matching failure. To solve this problem, we propose a new method to locate UAVs via shape and spatial relationship matching (SSRM) of buildings in urban scenes as an alternative to UAV localization via image matching. SSRM first extracts individual buildings from UAV images using the SOLOv2 instance segmentation algorithm. Then, these individual buildings are subsequently matched with vector e-map data (stored in .shp format) based on their shape and spatial relationship to determine their actual latitude and longitude. Control points are generated according to the matched buildings, and finally, the UAV position is determined. SSRM can efficiently realize high-precision UAV localization in urban scenes. Under the verification of actual data, SSRM achieves localization errors of 7.38 m and 11.92 m in downtown and suburb areas, respectively, with better localization performance than the radiation-variation insensitive feature transform (RIFT), channel features of the oriented gradient (CFOG), and SSM algorithms. Moreover, the SSRM algorithm exhibits a smaller localization error in areas with higher building density. Full article
(This article belongs to the Special Issue High-Precision and High-Reliability Positioning, Navigation, and Timing: Opportunities and Challenges)
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20 pages, 18835 KiB  
Article
Closed-Form Method for Unified Far-Field and Near-Field Localization Based on TDOA and FDOA Measurements
by Weishuang Gong, Xuan Song, Chunyu Zhu, Qi Wang and Yachao Li
Remote Sens. 2024, 16(16), 3047; https://doi.org/10.3390/rs16163047 - 19 Aug 2024
Viewed by 734
Abstract
When the near-field and far-field information of a target is uncertain, it is necessary to choose a suitable localization method. The modified polar representation (MPR) method integrates the two scenarios and achieves a unified localization with direction of arrival (DOA) estimation in the [...] Read more.
When the near-field and far-field information of a target is uncertain, it is necessary to choose a suitable localization method. The modified polar representation (MPR) method integrates the two scenarios and achieves a unified localization with direction of arrival (DOA) estimation in the far field and position estimation in the near field. Previous studies have only proposed solutions for stationary environments and have not considered the motion factor. Therefore, this paper proposes a new unified positioning algorithm using multi-sensor time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements without prior target source information. The method represents the position of the target source using MPR and describes the localization problem as a weighted least squares (WLS) problem with two constraints. We first obtain the initial estimates by WLS without considering the constraints and then investigate a two-step error correction method based on the constraints. The first step corrects the initial estimate using the Taylor series expansion technique, and the second step corrects the DOA estimate in the previous step using the direct error compensation technique based on the properties of the second constraint. Simulation experiments show that the method is effective for the unified positioning of moving targets and can achieve the Cramer–Rao lower bound (CRLB). Full article
(This article belongs to the Special Issue High-Precision and High-Reliability Positioning, Navigation, and Timing: Opportunities and Challenges)
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17 pages, 2932 KiB  
Article
Cruise Speed Model Based on Self-Attention Mechanism for Autonomous Underwater Vehicle Navigation
by Xiaokai Mu, Yuanhang Yi, Zhongben Zhu, Lili Zhu, Zhuo Wang and Hongde Qin
Remote Sens. 2024, 16(14), 2580; https://doi.org/10.3390/rs16142580 - 14 Jul 2024
Viewed by 894
Abstract
This study proposes a cruise speed model based on the Self-Attention mechanism for speed estimation in Autonomous Underwater Vehicle (AUV) navigation systems. By utilizing variables such as acceleration, angle, angular velocity, and propeller speed as inputs, the Self-Attention mechanism is constructed using Long [...] Read more.
This study proposes a cruise speed model based on the Self-Attention mechanism for speed estimation in Autonomous Underwater Vehicle (AUV) navigation systems. By utilizing variables such as acceleration, angle, angular velocity, and propeller speed as inputs, the Self-Attention mechanism is constructed using Long Short-Term Memory (LSTM) for handling the above information, enhancing the model’s accuracy during persistent bottom-track velocity failures. Additionally, this study introduces the water-track velocity information to enhance the generalization capability of the network and improve its speed estimation accuracy. The sea trial experiment results indicate that compared to traditional methods, this model demonstrates higher accuracy and reliability with both position error and velocity error analysis when the used Pathfinder DVL fails, providing an effective solution for AUV combined navigation systems. Full article
(This article belongs to the Special Issue High-Precision and High-Reliability Positioning, Navigation, and Timing: Opportunities and Challenges)
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17 pages, 9960 KiB  
Article
A Comprehensive Signal Quality Assessment for BDS/Galileo/GPS Satellites and Signals
by Yijun Tian, Guorui Xiao, Rui Guo, Dongqing Zhao, Lu Zhang, Jie Xin, Jinglei Guo, Yuechao Han, Xuefan Du, Donghan He and Zheng Qin
Remote Sens. 2024, 16(13), 2277; https://doi.org/10.3390/rs16132277 - 21 Jun 2024
Viewed by 724
Abstract
With the modernization of global navigation satellite systems (GNSS), especially the rapid development of the BeiDou Navigation Satellite System (BDS), more observations of satellites and signals have become available. Using data of the globally distributed MGEX stations, a systematic and comprehensive evaluation of [...] Read more.
With the modernization of global navigation satellite systems (GNSS), especially the rapid development of the BeiDou Navigation Satellite System (BDS), more observations of satellites and signals have become available. Using data of the globally distributed MGEX stations, a systematic and comprehensive evaluation of signal characteristics for BDS-3, BDS-2, GPS, and Galileo is conducted in terms of carrier-to-noise ratio (C/N0), code noise, and multipath in the contribution. First, a comprehensive signal quality assessment method for BDS/Galileo/GPS satellites and signals is proposed, including C/N0 modeling and MP modeling. For BDS, the BDS-3 satellites apparently have higher signal power than the BDS-2 satellites at the same frequency such as B1I and B3I, and the signal B2a of BDS-3 is superior to other signals in regard to signal power, which is comparable with the superior Galileo E5 signals and GPS L5. Among all the signals, the observation accuracy of E5 is the highest regardless of receiver types, and next highest are BDS-3 B2a and GPS L5. Due to not being affected by the systematic code errors of BDS-2, the observations of BDS-3 satellites contain smaller multipath errors than that of BDS-2 satellites. As for the multipath suppression performance, the BDS-3 signal B2a, GPS L5, and Galileo E5 and E5b perform better than the other signals, which may be related to their wide signal bandwidths. Full article
(This article belongs to the Special Issue High-Precision and High-Reliability Positioning, Navigation, and Timing: Opportunities and Challenges)
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19 pages, 4105 KiB  
Article
Integration of High-Rate GNSS and Strong Motion Record Based on Sage–Husa Kalman Filter with Adaptive Estimation of Strong Motion Acceleration Noise Uncertainty
by Yuanfan Zhang, Zhixi Nie, Zhenjie Wang, Guohong Zhang and Xinjian Shan
Remote Sens. 2024, 16(11), 2000; https://doi.org/10.3390/rs16112000 - 1 Jun 2024
Viewed by 884
Abstract
A strong motion seismometer is a kind of inertial sensor, and it can record middle- to high-frequency ground accelerations. The double-integration from acceleration to displacement amplifies errors caused by tilt, rotation, hysteresis, non-linear instrument response, and noise. This leads to long-period, non-physical baseline [...] Read more.
A strong motion seismometer is a kind of inertial sensor, and it can record middle- to high-frequency ground accelerations. The double-integration from acceleration to displacement amplifies errors caused by tilt, rotation, hysteresis, non-linear instrument response, and noise. This leads to long-period, non-physical baseline drifts in the integrated displacements. GNSS enables the direct observation of the ground displacements, with an accuracy of several millimeters to centimeters and a sample rate of 1 Hz to 50 Hz. Combining GNSS and a strong motion seismometer, one can obtain an accurate displacement series. Typically, a Kalman filter is adopted to integrate GNSS displacements and strong motion accelerations, using the empirical values of noise uncertainty. Considering that there are significantly different errors introduced by the above-mentioned tilt, rotation, hysteresis, and non-linear instrument response at different stations or at different times at the same station, it is inappropriate to employ a fixed noise uncertainty for strong motion accelerations. In this paper, we present a Sage–Husa Kalman filter, where the noise uncertainty of strong motion acceleration is adaptively estimated, to integrate GNSS and strong motion acceleration for obtaining the displacement series. The performance of the proposed method was validated by a shake table simulation experiment and the GNSS/strong motion co-located stations collected during the 2023 Mw 7.8 and Mw 7.6 earthquake doublet in southeast Turkey. The experimental results show that the proposed method enhances the adaptability to the variation of strong motion accelerometer noise level and improves the precision of integrated displacement series. The displacement derived from the proposed method was up to 28% more accurate than those from the Kalman filter in the shake table test, and the correlation coefficient with respect to the references arrived at 0.99. The application to the earthquake event shows that the proposed method can capture seismic waveforms at a promotion of 46% and 23% in the horizontal and vertical directions, respectively, compared with the results of the Kalman filter. Full article
(This article belongs to the Special Issue High-Precision and High-Reliability Positioning, Navigation, and Timing: Opportunities and Challenges)
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29 pages, 43946 KiB  
Article
A Low-Cost 3D SLAM System Integration of Autonomous Exploration Based on Fast-ICP Enhanced LiDAR-Inertial Odometry
by Conglin Pang, Liqing Zhou and Xianfeng Huang
Remote Sens. 2024, 16(11), 1979; https://doi.org/10.3390/rs16111979 - 30 May 2024
Cited by 3 | Viewed by 1865
Abstract
Advancements in robotics and mapping technology have spotlighted the development of Simultaneous Localization and Mapping (SLAM) systems as a key research area. However, the high cost of advanced SLAM systems poses a significant barrier to research and development in the field, while many [...] Read more.
Advancements in robotics and mapping technology have spotlighted the development of Simultaneous Localization and Mapping (SLAM) systems as a key research area. However, the high cost of advanced SLAM systems poses a significant barrier to research and development in the field, while many low-cost SLAM systems, operating under resource constraints, fail to achieve high-precision real-time mapping and localization, rendering them unsuitable for practical applications. This paper introduces a cost-effective SLAM system design that maintains high performance while significantly reducing costs. Our approach utilizes economical components and efficient algorithms, addressing the high-cost barrier in the field. First, we developed a robust robotic platform based on a traditional four-wheeled vehicle structure, enhancing flexibility and load capacity. Then, we adapted the SLAM algorithm using the LiDAR-inertial Odometry framework coupled with the Fast Iterative Closest Point (ICP) algorithm to balance accuracy and real-time performance. Finally, we integrated the 3D multi-goal Rapidly exploring Random Tree (RRT) algorithm with Nonlinear Model Predictive Control (NMPC) for autonomous exploration in complex environments. Comprehensive experimental results confirm the system’s capability for real-time, autonomous navigation and mapping in intricate indoor settings, rivaling more expensive SLAM systems in accuracy and efficiency at a lower cost. Our research results are published as open access, facilitating greater accessibility and collaboration. Full article
(This article belongs to the Special Issue High-Precision and High-Reliability Positioning, Navigation, and Timing: Opportunities and Challenges)
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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 765
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
(This article belongs to the Special Issue High-Precision and High-Reliability Positioning, Navigation, and Timing: Opportunities and Challenges)
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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
Cited by 1 | Viewed by 1404
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
(This article belongs to the Special Issue High-Precision and High-Reliability Positioning, Navigation, and Timing: Opportunities and Challenges)
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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 1643
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
(This article belongs to the Special Issue High-Precision and High-Reliability Positioning, Navigation, and Timing: Opportunities and Challenges)
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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 3 | Viewed by 2165
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
(This article belongs to the Special Issue High-Precision and High-Reliability Positioning, Navigation, and Timing: Opportunities and Challenges)
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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 1123
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
(This article belongs to the Special Issue High-Precision and High-Reliability Positioning, Navigation, and Timing: Opportunities and Challenges)
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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 3 | Viewed by 2165
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
(This article belongs to the Special Issue High-Precision and High-Reliability Positioning, Navigation, and Timing: Opportunities and Challenges)
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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 1390
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
(This article belongs to the Special Issue High-Precision and High-Reliability Positioning, Navigation, and Timing: Opportunities and Challenges)
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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 1274
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
(This article belongs to the Special Issue High-Precision and High-Reliability Positioning, Navigation, and Timing: Opportunities and Challenges)
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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 1343
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
(This article belongs to the Special Issue High-Precision and High-Reliability Positioning, Navigation, and Timing: Opportunities and Challenges)
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