Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.6
4.1
Journals
Remote Sensing
Special Issues
GNSS and Multi-Sensor Integrated Precise Positioning and Applications
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 and Multi-Sensor Integrated Precise Positioning and Applications

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 5980

Special Issue Editors

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; deep learning for GNSS
Special Issues, Collections and Topics in MDPI journals
Dr. Xinghan Chen

E-Mail Website
Guest Editor
Institute of Geodesy, Faculty VI—Planning Building Environment, Technische Universität Berlin, Kaiserin-Augusta-Allee 104, 10553 Berlin, Germany
Interests: receiver clock modeling; precise orbit determination; ionosphere; earthquake; integrity monitoring
Dr. Peiyuan Zhou

E-Mail Website
Guest Editor
College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China
Interests: low-cost and high-precision GNSS positioning and timing; GNSS/INS integration
Dr. Lingxuan Wang

E-Mail Website
Guest Editor Assistant
School of Resource, Environment and Engineering, Hubei University of Science and Technology, Xianning 430072, China
Interests: GNSS precise positioning; attitude determination; integrated navigation

Special Issue Information

Dear Colleagues,

Global Navigation Satellite Systems (GNSSs) are an indispensable positioning and navigation technology that is extensively utilized across various sectors, including aerospace, autonomous driving, intelligent transportation, and geological exploration. However, GNSSs’ positioning accuracy, availability, continuity, and reliability can significantly deteriorate in complex environments such as urban canyons and tunnels. The limitations of standalone GNSSs can be significantly alleviated by integrating alternative sensors due to recent advancements in Inertial Measurement Units (IMUs), visual sensors, radar, ultrasonic sensors, etc. Incorporating a variety of multi-source sensors into GNSSs has therefore become a standard practice in various fields, fully leveraging the strengths of diverse systems to address the shortcomings of individual sensors and providing improved positioning solutions with high accuracy, availability, continuity, and reliability in various complex environments.

This Special Issue aims to gather and highlight the latest research advancements in GNSSs and multi-sensor fusion technologies for precise positioning and applications. This Special Issue emphasizes the design and optimization of various fusion algorithms, assesses their effectiveness in challenging environments, and advocates for the practical applications of GNSSs and multi-sensor integration technologies.

In this Special Issue, original research articles and reviews are welcome. Topics may include (but are not limited to) the following research areas:

  • Methods and algorithms for GNSSs and multi-sensor integration.
  • Multi-sensor-assisted GNSS cycle slip detection and ambiguity resolution.
  • Positioning recovery techniques in the absence of GNSSs.
  • Applications of deep learning in GNSSs and multi-sensor integrated systems.
  • Cooperative positioning and path planning of the integrated systems.
  • Precise positioning strategies for integrated systems in challenging environments.
  • Hardware platform and data processing architecture for integrated systems.
  • Applications and achievements of integrated systems in autonomous driving, intelligent transportation, and robot navigation.

We look forward to receiving your contributions.

Dr. Guorui Xiao
Dr. Xinghan Chen
Dr. Peiyuan Zhou
Guest Editors

Dr. Lingxuan Wang
Guest Editor Assistant

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 250 words) can be sent to the Editorial Office for assessment.

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
  • multi-sensors integration
  • integration algorithms
  • cooperative positioning
  • challenging environments
  • precise positioning
  • robust positioning

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.

Published Papers (5 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

29 pages, 2906 KB  
Article
Robust High-Precision Time Synchronization for Distributed Sensor Systems in Challenging Environments
by Zhouji Wang, Daqian Lyu, Peiyuan Zhou, Yulong Ge, Yao Hu, Rangang Zhu, Wei Wang and Xiaoniu Yang
Remote Sens. 2025, 17(22), 3715; https://doi.org/10.3390/rs17223715 - 14 Nov 2025
Viewed by 555
Abstract
Timing and time synchronization are critical capabilities of Global Navigation Satellite Systems (GNSSs), but their performance deteriorates significantly in challenging environments like urban canyons and tunnels. To address this issue, this paper proposes the Distributed Sensor Time Synchronization architecture (DSTS), a novel architecture [...] Read more.
Timing and time synchronization are critical capabilities of Global Navigation Satellite Systems (GNSSs), but their performance deteriorates significantly in challenging environments like urban canyons and tunnels. To address this issue, this paper proposes the Distributed Sensor Time Synchronization architecture (DSTS), a novel architecture integrating Bayesian filtering with deep reinforcement learning. DSTS utilizes Bayesian filtering to fuse Time-of-Flight (ToF) measurements with Channel Impulse Response features for real-time compensation of non-linear errors and accurate path state prediction. Concurrently, the Deep Deterministic Policy Gradient (DDPG) algorithm trains each node into an intelligent agent that dynamically learns optimal synchronization weights based on local information like neighbor clock stability and link quality. This allows the architecture to adaptively amplify reliable nodes while mitigating the negative effects of unstable peers and adverse channels, ensuring high accuracy and availability. Simulation experiments based on a real-world UWB dataset demonstrate the architecture’s exceptional performance. The Bayesian filtering module effectively mitigates non-linear errors, reducing the standard deviation of ToF measurements in NLOS scenarios by up to 51.6% (over 41.2% consistently) while achieving high path state prediction accuracy (>85% static, >95% simulated dynamic). In simulated dynamic and heterogeneous networks, the DDPG algorithm achieves a synchronization accuracy better than traditional average-consensus algorithms, ultimately reaching a frequency and phase precision of 4×10−10 and 5×10−10 s, respectively. Full article
(This article belongs to the Special Issue GNSS and Multi-Sensor Integrated Precise Positioning and Applications)
►▼ Show Figures

Figure 1

21 pages, 1273 KB  
Article
Satellite Formation Flying Determination with Low-Cost GNSS Receivers Raw Data
by David Forero, Segundo Esteban and Oscar R. Polo
Remote Sens. 2025, 17(22), 3691; https://doi.org/10.3390/rs17223691 - 12 Nov 2025
Viewed by 363
Abstract
Low-cost missions are ideal for applications that require spacecraft formation flying. The use of GNSS signals provides an economical solution to determine the orbital status of the formation. This paper facilitates the development of such missions by simulating spacecraft orbital formation conditions through [...] Read more.
Low-cost missions are ideal for applications that require spacecraft formation flying. The use of GNSS signals provides an economical solution to determine the orbital status of the formation. This paper facilitates the development of such missions by simulating spacecraft orbital formation conditions through the use of software-defined radio to generate the GNSS signals being received by each spacecraft. The simulation environment integrates low-cost commercial GNSSs, one for each member of the formation, to capture the signals generated. The analysis of the recorded raw signals shows that the instrumental error of the receivers is predominant because they have not been designed to work in orbital conditions. In addition to noise, the bias errors introduced must be taken into account by the mathematical trilateration methods, which can be very sensitive to these errors. This paper shows how sensitivity can be quantified using the condition number for matrix inversion. A condition number analysis determines that the optimal solution for trilaterating the orbital position of a spacecraft should use as few GNSS satellites as possible. The paper also introduces how to use the condition number to evaluate different methods for determining the state of the spacecraft formation: the independent trilateration method, the difference method, and the double difference method. The comparison of the methods shows that the difference and double difference methods are more sensitive to instrumental errors, because they are worse conditioned, but can be improved by reducing their order. Despite the limitations shown, at best, errors in the relative positions of the spacecrafts of the order of metres are obtained, demonstrating the feasibility of this type of mission and the usefulness of the condition number analysis method presented. Full article
(This article belongs to the Special Issue GNSS and Multi-Sensor Integrated Precise Positioning and Applications)
►▼ Show Figures

Figure 1

21 pages, 2309 KB  
Article
LEO ISL-Assisted BDS-3 and LEO Rapid Joint Precise Orbit Determination
by Le Wang, Dandan Song, Wen Lai, Bobin Cui and Guanwen Huang
Remote Sens. 2025, 17(18), 3204; https://doi.org/10.3390/rs17183204 - 17 Sep 2025
Viewed by 759
Abstract
BDS-3 faces challenges in achieving precision orbit determination (POD) due to the difficulty of establishing a globally uniform distribution of independently operated ground tracking stations. The use of onboard BDS-3 observations collected by low Earth orbit (LEO) satellites can partially mitigate this limitation. [...] Read more.
BDS-3 faces challenges in achieving precision orbit determination (POD) due to the difficulty of establishing a globally uniform distribution of independently operated ground tracking stations. The use of onboard BDS-3 observations collected by low Earth orbit (LEO) satellites can partially mitigate this limitation. However, these observations introduce additional parameters, such as receiver clock offsets and carrier-phase ambiguities, which substantially increase the computational burden. Therefore, the capability of achieving real-time (RT) joint POD for BDS-3 and LEO satellites, relying solely on independently operated tracking stations, is greatly constrained. Currently, the inter-satellite links (ISLs) of BDS-3 have been successfully demonstrated to be effective for POD of BDS-3 satellites. In the future, ISLs of LEO satellites will also be incorporated as a measurement technique. Compared to traditional BDS-3 onboard observations, POD using ISLs involves almost no additional parameters other than the orbital states. Therefore, this paper proposes a method that combines onboard BDS-3 receivers on a subset of LEO satellites with LEO ISL observations to achieve rapid high-precision joint POD for BDS-3 and the full LEO constellation. To validate the proposed approach, measured BDS-3 data from regional ground stations in China are employed, together with simulated onboard BDS-3 data and simulated LEO ISL observations. All datasets were obtained over a three-day period, corresponding to days 131–133 of the year 2025. Firstly, it is demonstrated that, when relying solely on regional ground stations, the 24 MEO and 3 IGSO satellites of BDS-3 cannot achieve high-precision POD, with 1D RMS orbit accuracies of only 11.6 cm and 26.9 cm, respectively. Incorporating onboard BDS-3 data from LEO satellites significantly improves orbit determination accuracy, with 1D RMS accuracies reaching 4.9 cm for MEO and 6.4 cm for IGSO satellites, while LEO satellites themselves achieve orbit accuracy better than 5 cm. Subsequently, the computational burden introduced by onboard BDS-3 data from LEO satellites in joint POD is further assessed. On average, incorporating onboard BDS-3 data from 10 LEO satellites adds approximately 6780 parameters to be estimated, substantially increasing computation time. When onboard BDS-3 data from 20 LEO satellites are included, the achieved BDS-3 orbit accuracy shows negligible degradation compared to using data from all LEO satellites, with 1D RMS accuracies of 4.9 cm and 6.7 cm for MEO and IGSO, respectively. Meanwhile, the processing time for a single batch least squares (BLSQ) solution decreases dramatically from 27.0 min to 5.7 min. Increasing the number of LEO satellites to 30 further improves BDS-3 orbit accuracy, mainly due to the enhanced orbit precision of the LEO satellites. After incorporating LEO ISLs, LEO satellites achieve orbit accuracy in the 1D direction of approximately 1 cm, regardless of whether their onboard BDS-3 data are used. In summary, the proposed approach significantly reduces computational burden while ensuring orbit determination accuracy for both BDS-3 and LEO satellites. This approach is more likely to realize real-time joint POD of BDS-3 and LEO satellites based on large-scale LEO constellations. Full article
(This article belongs to the Special Issue GNSS and Multi-Sensor Integrated Precise Positioning and Applications)
►▼ Show Figures

Figure 1

28 pages, 9378 KB  
Article
A Semantic Segmentation-Based GNSS Signal Occlusion Detection and Optimization Method
by Zhe Yue, Chenchen Sun, Xuerong Zhang, Chengkai Tang, Yuting Gao and Kezhao Li
Remote Sens. 2025, 17(15), 2725; https://doi.org/10.3390/rs17152725 - 6 Aug 2025
Viewed by 2876
Abstract
Existing research fails to effectively address the problem of increased GNSS positioning errors caused by non-line-of-sight (NLOS) and line-of-sight (LOS) signal attenuation due to obstructions such as buildings and trees in complex urban environments. To address this issue, we dig into the environmental [...] Read more.
Existing research fails to effectively address the problem of increased GNSS positioning errors caused by non-line-of-sight (NLOS) and line-of-sight (LOS) signal attenuation due to obstructions such as buildings and trees in complex urban environments. To address this issue, we dig into the environmental perception perspective to propose a semantic segmentation-based GNSS signal occlusion detection and optimization method. The approach distinguishes between building and tree occlusions and adjusts signal weights accordingly to enhance positioning accuracy. First, a fisheye camera captures environmental imagery above the vehicle, which is then processed using deep learning to segment sky, tree, and building regions. Subsequently, satellite projections are mapped onto the segmented sky image to classify signal occlusions. Then, based on the type of obstruction, a dynamic weight optimization model is constructed to adjust the contribution of each satellite in the positioning solution, thereby enhancing the positioning accuracy of vehicle-navigation in urban environments. Finally, we construct a vehicle-mounted navigation system for experimentation. The experimental results demonstrate that the proposed method enhances accuracy by 16% and 10% compared to the existing GNSS/INS/Canny and GNSS/INS/Flood Fill methods, respectively, confirming its effectiveness in complex urban environments. Full article
(This article belongs to the Special Issue GNSS and Multi-Sensor Integrated Precise Positioning and Applications)
►▼ Show Figures

Figure 1

24 pages, 4653 KB  
Article
A Multi-Receiver GNSS System Geometry Control Algorithm in Mobile Measurement of Railway Track Axis Position
by Jacek Skibicki, Andrzej Wilk, Władysław Koc, Piotr Chrostowski, Roksana Licow, Paweł Szymon Dąbrowski, Krzysztof Karwowski, Sławomir Judek, Michał Michna, Jacek Szmagliński and Sławomir Grulkowski
Remote Sens. 2025, 17(14), 2461; https://doi.org/10.3390/rs17142461 - 16 Jul 2025
Viewed by 638
Abstract
Accurate diagnostics of the railway track axis are crucial for enabling higher operational speeds of rail vehicles. Consequently, assessment methods are under continuous refinement. The precision of mobile GNSS-based measurements is influenced by external factors such as terrain obstructions and satellite geometry. This [...] Read more.
Accurate diagnostics of the railway track axis are crucial for enabling higher operational speeds of rail vehicles. Consequently, assessment methods are under continuous refinement. The precision of mobile GNSS-based measurements is influenced by external factors such as terrain obstructions and satellite geometry. This study presents an innovative approach that continuously monitors a predefined multi-receiver GNSS configuration in real time to enhance positioning accuracy. Field experiments on operational railway lines demonstrate that the proposed method significantly improves measurement reliability and reduces uncertainty compared to the conventional CQ-based quality assessment. The algorithm underlying the proposed method is formulated through analytical equations, allowing it to be implemented in any programming environment. The method’s effectiveness is demonstrated by comparing the expanded uncertainty calculated using GNSS-provided data with that obtained through the proposed approach. Full article
(This article belongs to the Special Issue GNSS and Multi-Sensor Integrated Precise Positioning and Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop