Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
GNSS Advanced Positioning Algorithms and Innovative Applications (2nd Edition)
remotesensing-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

GNSS Advanced Positioning Algorithms and Innovative Applications (2nd Edition)

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

Deadline for manuscript submissions: 30 November 2025 | Viewed by 1422

Special Issue Editors

Dr. Xianlu Tao

E-Mail Website
Guest Editor
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
Interests: GNSS high-precision positioning; multi-sensors integrated navigation and its applications
Dr. Rui Sun

E-Mail Website
Guest Editor
College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Interests: seamless positioning and navigation in challenging environments
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Global Navigation Satellite Systems (GNSSs) are critical components within the integrated positioning, navigation, and timing (PNT) framework, serving as the foundation for high-precision positioning across diverse industries. GNSS-based solutions provide meter- and centimeter-level accuracy through code and carrier phase observations, supporting applications ranging from everyday navigation to cutting-edge autonomous technologies. However, as the demand for more robust, seamless, and reliable positioning increases, especially in challenging environments such as urban canyons and under non-line-of-sight (NLOS) conditions, there is a growing need for innovation in GNSS processing and fusion with other sensors.

This Special Issue highlights advancements in multi-source GNSS fusion for seamless positioning, including developments in intelligent multipath and NLOS processing, reliable ambiguity resolution, and the online assessment of positioning integrity. We also welcome contributions that explore the application of machine learning in GNSSs, leveraging its potential to enhance high-precision performance on smartphones and in autonomous driving. Furthermore, submissions related to experimental demonstrations of multi-source positioning systems and the role of GNSSs in future integrated PNT frameworks will be highly valued.

Dr. Xianlu Tao
Dr. Rui Sun
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 high-precision positioning and navigation
  • multi-source fusion seamless positioning and navigation
  • intelligent processing of multipath and NLOS
  • reliable resolution of GNSS ambiguities
  • online evaluation of positioning integrity
  • machine learning in high-precision GNSS applications
  • high-precision GNSS applications for smartphones
  • high-precision GNSS applications for autonomous driving

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 2273 KiB  
Article
Rapid Deformation Identification and Adaptive Filtering with GNSS TDCP Under Different Scenarios and Its Application in Landslide Monitoring
by Mingkui Wu, Rui Wen, Yue Zhang and Wanke Liu
Remote Sens. 2025, 17(10), 1751; https://doi.org/10.3390/rs17101751 - 17 May 2025
Viewed by 81
Abstract
Global navigation satellite system (GNSS) real-time kinematic (RTK) has been widely applied in landslide monitoring and warning, since it can provide real-time and high-precision three-dimensional deformation information in all weather and all the time. The Kalman filter is often adopted for parameter estimation [...] Read more.
Global navigation satellite system (GNSS) real-time kinematic (RTK) has been widely applied in landslide monitoring and warning, since it can provide real-time and high-precision three-dimensional deformation information in all weather and all the time. The Kalman filter is often adopted for parameter estimation in GNSS RTK positioning since it can effectively suppress the observational noise and improve the positioning accuracy and reliability. However, the discrepancy between the empirical state model in the Kalman filter and the actual state of the monitoring object could lead to large positioning errors or even the divergence of the Kalman filter. In this contribution, we propose a novel rapid deformation identification and adaptive filtering approach with GNSS time-differenced carrier phase (TDCP) under different scenarios for landslide monitoring. We first present the methodology of the proposed TDCP-based rapid deformation identification and adaptive filtering approach for GNSS RTK positioning. The effectiveness of the proposed approach is then validated with a simulated displacement experiment with a customized three-dimensional displacement platform. The experimental results demonstrate that the proposed approach can accurately and promptly identify the rapid between-epoch deformation of more than approximately 1.5 cm and 3.0 cm for the horizontal and vertical components for the monitoring object under a complex observational environment. Meanwhile, it can effectively suppress the observational noise and thus maintain mm-to-cm-level monitoring accuracy. The proposed approach can provide high-precision and reliable three-dimensional deformation information for GNSS landslide monitoring and early warning. Full article
(This article belongs to the Special Issue GNSS Advanced Positioning Algorithms and Innovative Applications (2nd Edition))
►▼ Show Figures

Figure 1

21 pages, 7046 KiB  
Article
High-Precision Multi-Source Fusion Navigation Solutions for Complex and Dynamic Urban Environments
by Long Li, Wenfeng Nie, Wenpeng Zong, Tianhe Xu, Mowen Li, Nan Jiang and Wei Zhang
Remote Sens. 2025, 17(8), 1371; https://doi.org/10.3390/rs17081371 - 11 Apr 2025
Viewed by 292
Abstract
With the rapid advancement of artificial intelligence, particularly in fields such as autonomous driving, drone delivery, and logistics automation, the demand for high-precision and robust navigation services has become critical. In complex and dynamic urban environments, the navigation capabilities of single-sensor systems struggle [...] Read more.
With the rapid advancement of artificial intelligence, particularly in fields such as autonomous driving, drone delivery, and logistics automation, the demand for high-precision and robust navigation services has become critical. In complex and dynamic urban environments, the navigation capabilities of single-sensor systems struggle to meet the practical requirements of autonomous driving technology. To address this issue, we propose a multi-source fusion navigation algorithm tailored for dynamic urban canyon scenarios, aiming to achieve reliable and continuous state estimation in complex environments. In our proposed method, we utilize independent threads on a graphics processing unit (GPU) to perform real-time detection and removal of dynamic objects in visual images, thereby enhancing the visual accuracy of multi-source fusion navigation in dynamic scenes. To tackle the challenges of significant Global Navigation Satellite System (GNSS) positioning errors and limited satellite availability in urban canyon environments, we introduce a specialized GNSS Real-Time Kinematic (RTK) stochastic model for such settings. The navigation performance of the proposed algorithm was evaluated using public datasets. The results demonstrate that our RTK/INS/Vision integrated navigation algorithm effectively improves both accuracy and availability in dynamic urban canyon environments. Full article
(This article belongs to the Special Issue GNSS Advanced Positioning Algorithms and Innovative Applications (2nd Edition))
►▼ Show Figures

Graphical abstract

19 pages, 5535 KiB  
Article
Global Navigation Satellite System-Based Deformation Monitoring of Hydraulic Structures Using a Gated Recurrent Unit–Attention Mechanism
by Haiyang Li, Yilin Xie, Azhong Dong, Jianping Xu, Xun Lu, Jinfeng Ding and Yan Zi
Remote Sens. 2025, 17(8), 1352; https://doi.org/10.3390/rs17081352 - 10 Apr 2025
Viewed by 280
Abstract
Accurate monitoring of ground deformation is crucial for ensuring the safety and stability of hydraulic structures. Current deformation monitoring techniques often face challenges such as limited accuracy and robustness, particularly in complex environments. In this study, we propose a comprehensive method for Global [...] Read more.
Accurate monitoring of ground deformation is crucial for ensuring the safety and stability of hydraulic structures. Current deformation monitoring techniques often face challenges such as limited accuracy and robustness, particularly in complex environments. In this study, we propose a comprehensive method for Global Navigation Satellite System (GNSS) deformation monitoring in hydraulic structures by integrating the strengths of Gated Recurrent Units (GRUs) and Autoregressive Attention mechanisms. This approach enables efficient modeling of long-term dependencies while focusing on critical time steps, thereby enhancing prediction accuracy and robustness, especially in multi-step forecasting tasks. Experimental results show that the proposed GRU–Attention model achieves millimeter-level multi-step prediction accuracy, with predictions closely matching actual deformation data. Compared to the traditional method, the GRU–Attention model improves prediction accuracy by approximately 37%. The model’s attention mechanism effectively captures both short-term variations and long-term trends, ensuring accurate predictions even in complex scenarios. This research advances the field of GNSS deformation monitoring for hydraulic structures, providing valuable insights for engineering decision-making and risk management, ultimately contributing to enhanced infrastructure safety. Full article
(This article belongs to the Special Issue GNSS Advanced Positioning Algorithms and Innovative Applications (2nd Edition))
►▼ Show Figures

Figure 1

23 pages, 7822 KiB  
Article
Crowdsourcing User-Enhanced PPP-RTK with Weighted Ionospheric Modeling
by Qing Zhao, Shuguo Pan, Wang Gao, Xianlu Tao, Hao Liu and Zeyu Zhang
Remote Sens. 2025, 17(6), 1099; https://doi.org/10.3390/rs17061099 - 20 Mar 2025
Viewed by 300
Abstract
In the conventional PPP-RTK mode, the platform and users act only as the generator and the utilizer of ionospheric corrections, respectively. In sparse reference station networks or regions with an active ionosphere, high-precision modeling still faces challenges. This study utilizes the concept of [...] Read more.
In the conventional PPP-RTK mode, the platform and users act only as the generator and the utilizer of ionospheric corrections, respectively. In sparse reference station networks or regions with an active ionosphere, high-precision modeling still faces challenges. This study utilizes the concept of crowdsourcing and treats users as dynamic reference stations. By continuously feeding back ionospheric information to the platform, high-spatial-resolution modeling is achieved. Additionally, weight factors related to user positions are incorporated into conventional polynomial models to transform the regional ionosphere model from a common model into customized models, thereby providing more personalized services for different users. Validation was conducted with a sparse reference network with an average inter-station distance of approximately 391 km. While increasing the number of crowdsourcing users generally improves modeling performance, the enhancement also depends on their spatial distribution; that is, crowdsourcing users primarily provide localized improvements in their vicinity. Therefore, crowdsourcing users should ideally be uniformly distributed across the whole network. Compared with the conventional common model, the proposed customized model can more effectively characterize the irregular physical characteristics of the ionosphere, and the modeling accuracy is improved by about 12% to 41% in different scenarios. Furthermore, the performance of single-frequency PPP-RTK was verified on the terminal. In general, both crowdsourcing enhancement and the customized model can accelerate the convergence speed of the float solutions and improve positioning accuracy to varying degrees, and the epoch fix rate of the fixed solutions is also significantly improved. Full article
(This article belongs to the Special Issue GNSS Advanced Positioning Algorithms and Innovative Applications (2nd Edition))
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop