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High-Precision Urban Positioning: GNSS and Multi-Sensor Fusion Technologies

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

Deadline for manuscript submissions: closed (28 February 2026) | Viewed by 1533

Special Issue Editors


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Guest Editor
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Interests: 3D computer vision; point cloud data processing; GNSS/INS/visual fusion positioning and its applications in intelligent driving; intelligent transportation; smart cities; digital twin

E-Mail Website
Guest Editor
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
Interests: intelligent driving and vehicle–road collaboration; navigation and location services; robots; image processing and machine vision; mapping and positioning technologies

Special Issue Information

Dear Colleagues,

Urban environments present unique challenges for high-precision positioning due to factors such as signal obstruction, multipath effects, and GNSS signal degradation caused by buildings and infrastructure. While Global Navigation Satellite Systems (GNSSs) have become a cornerstone for positioning and navigation, their performance in dense urban settings is often compromised. To overcome these limitations, there has been increasing interest in integrating GNSSs with complementary sensing technologies—such as inertial measurement units (IMUs), LiDAR, cameras, 5G signals, and maps—through advanced multi-sensor fusion methods.

From a remote sensing perspective, accurate geolocation is a critical prerequisite for a wide range of applications, including mobile mapping, UAV-based data acquisition, remote sensing image registration, change detection, and spatiotemporal monitoring. The fusion of GNSS with other sensors enhances the accuracy and reliability of spatial referencing, which directly affects the quality of Earth observation and geospatial data products. Additionally, emerging research explores how remote sensing data itself (e.g., 3D building models, high-resolution aerial imagery, or DEMs) can assist in GNSS correction, positioning constraints, and non-line-of-sight detection—illustrating a growing synergy between the two disciplines.

The goal of this Special Issue is to present the latest developments, methodologies, and applications in the field of high-precision urban positioning with a focus on GNSS and multi-sensor fusion technologies. We aim to bring together contributions that address the theoretical foundations, system design, data processing techniques, and real-world applications of these technologies in complex urban settings.

This Special Issue aligns closely with the scope of Remote Sensing, which emphasizes the development and application of sensing technologies and geospatial data processing. Contributions are expected to leverage remote sensing data or techniques, with an emphasis on high-precision, urban-oriented methods and systems.

  • GNSS positioning in urban environments;
  • Multi-source fusion for navigation (e.g., GNSS + IMU, LiDAR, camera, UWB, 5G, Map);
  • Positioning in GNSS-challenged or -denied environments;
  • Sensor Integration Technology;
  • Real-time kinematic (RTK) and Precise Point Positioning (PPP) improvements;
  • Machine learning and AI for urban positioning enhancement;
  • Integration of crowdsourced geospatial data for improved positioning;
  • Integration of remote sensing products and positioning techniques;
  • Urban feature extraction or 3D reconstruction enhanced by precise positioning;
  • Novel GNSS-based methodologies for geospatial data acquisition and analysis;
  • Remote sensing for urban mobility and smart city applications.

Dr. Hongjuan Zhang
Prof. Dr. Bijun Li
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

  • global navigation satellite system
  • GNSS/INS integration
  • simultaneous localization and mapping (SLAM)
  • multi-source fusion
  • urban positioning
  • deep learning-based positioning
  • urban location-based services

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Published Papers (2 papers)

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Research

18 pages, 7782 KB  
Article
Reduced-Dynamic Orbit Determination of Low-Orbit Satellites Taking into Account GNSS Attitude Errors
by Liang Liu, Yuhao Liu, Yibiao Chen and Chuang Qian
Remote Sens. 2026, 18(2), 373; https://doi.org/10.3390/rs18020373 - 22 Jan 2026
Viewed by 392
Abstract
Satellite attitude is critical for both satellite antenna phase center offset and phase wind-up correction. However, during the eclipse season, the nominal satellite attitude is almost impossible to maintain, and the satellite attitude variability affects the geometric distance correction of GNSS-LEO satellites, which [...] Read more.
Satellite attitude is critical for both satellite antenna phase center offset and phase wind-up correction. However, during the eclipse season, the nominal satellite attitude is almost impossible to maintain, and the satellite attitude variability affects the geometric distance correction of GNSS-LEO satellites, which ultimately affects the orbital accuracy of LEO satellites. To explore the impact of neglecting eclipsing attitude models on LEO satellite orbit determination, this study utilizes the attitude quaternion products provided by CNES to analyze the discrepancies between nominal attitude yaw angles and attitude quaternion-derived yaw angles. It also examines the variations in phase center offset and phase wind-up corrections, caused by neglecting eclipsing attitude models. The model is validated through orbit determination tests using onboard GRACE-FO data from days 90 to 109 of 2023. Based on these analyses, a simplified reduced-dynamic orbit determination model for LEO satellites using attitude quaternion is proposed. It is found that the phase residuals of GRACE-C and GRACE-D under the attitude quaternion strategy are reduced by 3.6% and 3.9%, respectively, and the orbital accuracies of GRACE-C and GRACE-D are improved by 7.3% and 4.5%, respectively, compared with the nominal attitude. Full article
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19 pages, 5002 KB  
Article
Deep Learning-Based Diffraction Identification and Uncertainty-Aware Adaptive Weighting for GNSS Positioning in Occluded Environments
by Chenhui Wang, Haoliang Shen, Yanyan Liu, Qingjia Meng and Chuang Qian
Remote Sens. 2026, 18(1), 158; https://doi.org/10.3390/rs18010158 - 3 Jan 2026
Viewed by 545
Abstract
In natural canyons and urban occluded environments, signal anomalies induced by the satellite diffraction effect are a critical error source affecting the positioning accuracy of deformation monitoring. This paper proposes a deep learning-based method for diffraction signal identification and mitigation. The method utilizes [...] Read more.
In natural canyons and urban occluded environments, signal anomalies induced by the satellite diffraction effect are a critical error source affecting the positioning accuracy of deformation monitoring. This paper proposes a deep learning-based method for diffraction signal identification and mitigation. The method utilizes a LSTM network to deeply mine the time-series characteristics of GNSS observation data. We systematically analyze the impact of azimuth, elevation, SNR, and multi-feature combinations on model recognition performance, demonstrating that single features suffer from incomplete information or poor discrimination. Experimental results show that the multi-dimensional feature scheme of “SNR + Elevation + Azimuth” effectively characterizes both signal strength and spatial geometric information, achieving complementary feature advantages. The overall recognition accuracy of the proposed method reaches 84.2%, with an accuracy of 88.0% for anomalous satellites that severely impact positioning precision. Furthermore, we propose an Adaptive Weighting Method for Diffraction Mitigation Based on Uncertainty Quantification. This method constructs a variance inflation model using the probability vector output from the LSTM Softmax layer and introduces Information Entropy to quantify prediction uncertainty, ensuring that the weighting model possesses protection capability when the model fails or is uncertain. In processing a set of GNSS data collected in a highly-occluded environment, the proposed method significantly outperforms traditional cut-off elevation and SNR mask strategies, improving the AFR to 99.9%, and enhancing the positioning accuracy in the horizontal and vertical directions by an average of 80.1% and 76.4%, respectively, thereby effectively boosting the positioning accuracy and reliability in occluded environments. Full article
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