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New Insights into Positioning and Navigation Technologies

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Aerospace Science and Engineering".

Deadline for manuscript submissions: closed (20 September 2023) | Viewed by 3376

Special Issue Editors


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Guest Editor
Navigation Research Center, Nanjing University of Aeronautics & Astronautics, Nanjing 211106, China
Interests: GNSS; visual navigation; integrated navigation; SINS; alignment

E-Mail Website
Guest Editor
School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China
Interests: visual navigation; integrated navigation; SLAM; robot

Special Issue Information

Dear Colleagues,

With the advancement of smart devices, the demand for ubiquitous navigation services is increasing rapidly. Although GNSS provides global navigation services, it faces challenges in complex environments, such as rejection, multipath interference, and so on. Multi-source information fusion, intelligent navigation, and other positioning and navigation technologies have developed rapidly to provide more ubiquitous navigation services.

This Special Issue aims to bring together the relevant researchers to consolidate the research already undertaken and chart future research directions. We invite you to contribute to the thematic series on Positioning and Navigation Technologies.

Prof. Dr. Qinghua Zeng
Dr. Weixing Qian
Dr. Changhui Jiang
Dr. Qian Meng
Guest Editors

Manuscript Submission Information

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Keywords

  • GNSS
  • SLAM
  • multi-source cooperative positioning and navigation
  • intelligent positioning and navigation
  • compensatory positioning and navigation
  • seamless positioning and navigation

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Published Papers (2 papers)

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Research

19 pages, 8793 KiB  
Article
Estimation and Compensation of Heading Misalignment Angle for Train SINS/GNSS Integrated Navigation System Based on Observability Analysis
by Wei Chen, Gongliu Yang and Yongqiang Tu
Appl. Sci. 2023, 13(21), 12085; https://doi.org/10.3390/app132112085 - 6 Nov 2023
Cited by 1 | Viewed by 1138
Abstract
The inertial Navigation Systems/global navigation satellite system (SINS/GNSS) has become a research hotspot in the field of train positioning. However, during a uniform straight-line motion period, the heading misalignment angle of the SINS/GNSS is unobservable, resulting in the divergence of the heading misalignment [...] Read more.
The inertial Navigation Systems/global navigation satellite system (SINS/GNSS) has become a research hotspot in the field of train positioning. However, during a uniform straight-line motion period, the heading misalignment angle of the SINS/GNSS is unobservable, resulting in the divergence of the heading misalignment angle and ultimately causing a divergence in the train’s speed and position estimation. To address this issue, this paper proposes an estimation and compensation method for the heading misalignment angle for train SINS/GNSS integrated navigation system based on an observability analysis. When the train enters a straight-line segment, the alignment of the train’s sideslip angle and the satellite velocity heading angle allows the achievement of velocity heading observation values that resolve the issue. In a curved segment, the heading angle becomes observable, allowing for an accurate estimation of the SINS’s heading misalignment angle using GNSS observations. The results showed that, whether the train is on a straight or curved track, the position estimation accuracy meets the simulation design criteria of 0.1 m, and the heading accuracy is better than 0.25°. In comparison to the results of pure GNSS position and velocity-assisted navigation, where heading divergence occurs during constant velocity straight-line segments, the method proposed in this paper not only converges but also achieves an accuracy comparable to the GNSS velocity-based heading alignment. The simulation results demonstrate that the proposed strategy significantly improves the accuracy of the heading misalignment angle estimation, thereby enhancing the accuracy of speed and position estimation under a GNSS-denied environment. Full article
(This article belongs to the Special Issue New Insights into Positioning and Navigation Technologies)
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15 pages, 7319 KiB  
Article
Visual–Inertial Navigation System Based on Virtual Inertial Sensors
by Yunpiao Cai, Weixing Qian, Jiaqi Zhao, Jiayi Dong and Tianxiao Shen
Appl. Sci. 2023, 13(12), 7248; https://doi.org/10.3390/app13127248 - 17 Jun 2023
Cited by 1 | Viewed by 1682
Abstract
In this paper, we propose a novel visual–inertial simultaneous localization and mapping (SLAM) method for intelligent navigation systems that aims to overcome the challenges posed by dynamic or large-scale outdoor environments. Our approach constructs a visual–inertial navigation system by utilizing virtual inertial sensor [...] Read more.
In this paper, we propose a novel visual–inertial simultaneous localization and mapping (SLAM) method for intelligent navigation systems that aims to overcome the challenges posed by dynamic or large-scale outdoor environments. Our approach constructs a visual–inertial navigation system by utilizing virtual inertial sensor components that are mapped to the torso IMU under different gait patterns through gait classification. We apply a zero-velocity update (ZUPT) to initialize the system with the original visual–inertial information. The pose information is then iteratively updated through nonlinear least squares optimization, incorporating additional constraints from the ZUPT to improve the accuracy of the system’s positioning and mapping capabilities in degenerate environments. Finally, the corrected pose information is fed into the solution. We evaluate the performance of our proposed SLAM method in three typical environments, demonstrating its applicability and high precision across various scenarios. Our method represents a significant advancement in the field of intelligent navigation systems and offers a promising solution to the challenges posed by degenerate environments. Full article
(This article belongs to the Special Issue New Insights into Positioning and Navigation Technologies)
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