Theory and Method of GNSS Precision Positioning and Its New Application

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: closed (15 October 2025) | Viewed by 4605

Special Issue Editors


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Guest Editor
School of Automation Science and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: autonomous navigation; multi-source information fusion; GNSS; human activity recognition; inertial navigation
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Electronic and Information Engineering, Beihang University, Beijing100191, China
Interests: pedestrian inertial positioning; wearable sensor-based positioning; motion recognition
Special Issues, Collections and Topics in MDPI journals
School of Automation Science and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: intelligent manufacturing; artificial intelligence; deep learning; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to provide a comprehensive platform for researchers, engineers, and scholars in the field of Global Navigation Satellite System (GNSS) precision positioning to promote in-depth discussions, share the latest research findings, and explore innovative applications related to GNSS precision positioning. By doing so, it hopes to drive the continuous development and improvement of GNSS technology, enhancing its performance and expanding its scope of application in various industries.

This Special Issue welcomes the submission of articles that address various topics related to GNSS precision positioning. It includes both theoretical research regarding improvements in the accuracy, reliability, and integrity of GNSS positioning, as well as practical methods for implementing high-precision positioning in different scenarios. The articles published in this Special Issue will bridge the gap between academic research and real-world applications, aiming to enhance the accessibility and utility of GNSS precision positioning in diverse fields. 

The scope of this Special Issue includes, but is not limited to, the following topics: 

  • Advanced GNSS positioning algorithms, such as new signal processing techniques for better ambiguity resolution and more accurate coordinate estimation.
  • Multi-system and multi-frequency integration in GNSS, exploring how to combine different satellite navigation systems and frequencies to improve positioning performance.
  • GNSS precise positioning in challenging environments, such as urban canyons, forests, and indoor spaces, and the corresponding solutions to overcome signal blockage and interference.
  • The application of GNSS precision positioning in areas such as autonomous driving, surveying and mapping, deformation monitoring, smart city, electricity inspection, and smart agriculture, highlighting the practical value and impact of this technology.

Dr. Qu Wang
Dr. Ming Xia
Dr. Meixia Fu
Guest Editors

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Keywords

  • GNSS precision positioning
  • positioning algorithms
  • multi-system integration
  • environmental impact
  • application fields

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Published Papers (4 papers)

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Research

18 pages, 6335 KB  
Article
Real-Time Estimation of Ionospheric Power Spectral Density for Enhanced BDS PPP/PPP-AR Performance
by Yixi Wang, Huizhong Zhu, Qi Xu, Jun Li and Chuanfeng Song
Electronics 2025, 14(21), 4342; https://doi.org/10.3390/electronics14214342 - 5 Nov 2025
Viewed by 267
Abstract
The undifferenced and uncombined (UDUC) model preserves raw code and carrier-phase observations for each frequency, avoiding differencing or ionosphere-free combinations. This approach enables the direct estimation of atmospheric parameters. However, the stochastic characteristics of these parameters, particularly ionospheric delay, are often oversimplified or [...] Read more.
The undifferenced and uncombined (UDUC) model preserves raw code and carrier-phase observations for each frequency, avoiding differencing or ionosphere-free combinations. This approach enables the direct estimation of atmospheric parameters. However, the stochastic characteristics of these parameters, particularly ionospheric delay, are often oversimplified or based on empirical assumptions, limiting the accuracy and convergence speed of Precise Point Positioning (PPP). To address this issue, this study introduces a stochastic constraint model based on the power spectral density (PSD) of ionospheric variations. The PSD describes the distribution of ionospheric delay variance across temporal frequencies, thereby providing a physically meaningful constraint for modeling their temporal correlations. Integrating this PSD-derived stochastic model into the UDUC framework improves both ionospheric delay estimation and PPP performance, especially under disturbed ionospheric conditions. This paper presents a BDS PPP/PPP-AR method that estimates the ionospheric power spectral density (IPSD) in real time. Vondrak smoothing is applied to suppress noise in ionospheric observations before IPSD estimation. Experimental results demonstrate that the proposed approach significantly improves convergence time and positioning accuracy. Compared to the empirical IPSD model, the PPP mode using the estimated IPSD reduced horizontal and vertical convergence times by 11.1% and 13.2%, and improved the corresponding accuracies by 15.7% and 12.6%, respectively. These results confirm that real-time IPSD estimation, coupled with Vondrak smoothing, establishes an adaptive and robust ionospheric modeling framework that enhances BDS PPP and PPP-AR performance under varying ionospheric conditions. Full article
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25 pages, 2748 KB  
Article
A Low-Complexity Forward–Backward Filtering Algorithm for Real-Time GNSS Deformation Monitoring at the Edge
by Ling Huang, Da Li, Huangyi Yan, Kaixin Wang and Zhangqin Huang
Electronics 2025, 14(12), 2388; https://doi.org/10.3390/electronics14122388 - 11 Jun 2025
Viewed by 887
Abstract
Real-time Global Navigation Satellite System (GNSS) deformation monitoring is crucial for structural safety but is challenged by long-term, high-amplitude noise and trend-like anomalies. To address these issues, we propose a low-complexity forward–backward reliable filtering algorithm (FBRFF) tailored for edge environments. FBRFF integrates trend-aware [...] Read more.
Real-time Global Navigation Satellite System (GNSS) deformation monitoring is crucial for structural safety but is challenged by long-term, high-amplitude noise and trend-like anomalies. To address these issues, we propose a low-complexity forward–backward reliable filtering algorithm (FBRFF) tailored for edge environments. FBRFF integrates trend-aware correction and confidence-based fusion within a sliding window framework to effectively suppress non-stationary disturbances while preserving true deformation signals. Its architecture compensates for the inadequate performance of existing filters under persistent large-amplitude noise, enabling early anomaly correction before deformation analysis. In addition, it significantly reduces system computational load and complexity when processing massive multi-source data, while allowing easy integration with other filtering algorithms for enhanced robustness. Experiments using real-world GNSS data from the Usnisa Palace monitoring project validate that FBRFF improves positioning accuracy by up to 82% over baseline methods and maintains real-time responsiveness on resource-constrained platforms. These results demonstrate that FBRFF provides a lightweight, robust, and scalable solution for real-time GNSS monitoring, offering practical value for early-warning systems and infrastructure safety management. Full article
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21 pages, 3087 KB  
Article
Statistical Modeling of PPP-RTK Derived Ionospheric Residuals for Improved ARAIM MHSS Protection Level Calculation
by Tiantian Tang, Yan Xiang, Sijie Lyu, Yifan Zhao and Wenxian Yu
Electronics 2025, 14(12), 2340; https://doi.org/10.3390/electronics14122340 - 7 Jun 2025
Viewed by 878
Abstract
Ensuring Global Navigation Satellite System (GNSS) integrity, which provides operational reliability via fault detection, is important for safety-critical applications using high-precision techniques like Precise Point Positioning (PPP) and Real-Time Kinematic (RTK). Ionospheric errors, from atmospheric free electrons, challenge this integrity by introducing variable [...] Read more.
Ensuring Global Navigation Satellite System (GNSS) integrity, which provides operational reliability via fault detection, is important for safety-critical applications using high-precision techniques like Precise Point Positioning (PPP) and Real-Time Kinematic (RTK). Ionospheric errors, from atmospheric free electrons, challenge this integrity by introducing variable uncertainties into positioning solutions. This study investigates how ionospheric error modeling spatial resolution impacts protection level (PL) calculations, a metric defining positioning error bounds with high confidence. A comparative evaluation was conducted in low-latitude (Guangdong) and mid-latitude (Shandong) regions, contrasting large-scale with small-scale grid-based ionospheric models from regional GNSS networks. Experimental results show small-scale grids improve characterization of localized ionospheric variability, reducing ionospheric residual standard deviation by approximately 30% and enhancing PL precision. Large-scale grids show limitations, especially in active low-latitude conditions, leading to conservative PLs that reduce system availability and increase missed fault detection risks. A user-side PL computation framework incorporating this high-resolution ionospheric residual uncertainty improved system availability to 94.7% and lowered misleading and hazardous outcomes by over 80%. This research indicates that refined, high-resolution ionospheric modeling improves operational reliability and safety for high-integrity GNSS applications, particularly under diverse and challenging ionospheric conditions. Full article
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17 pages, 7673 KB  
Article
Motion Pattern Recognition via CNN-LSTM-Attention Model Using Array-Based Wi-Fi CSI Sensors in GNSS-Denied Areas
by Ming Xia, Shengmao Que, Nanzhu Liu, Qu Wang and Tuan Li
Electronics 2025, 14(8), 1594; https://doi.org/10.3390/electronics14081594 - 15 Apr 2025
Cited by 2 | Viewed by 2200
Abstract
Human activity recognition (HAR) is vital for applications in fields such as smart homes, health monitoring, and navigation, particularly in GNSS-denied environments where satellite signals are obstructed. Wi-Fi channel state information (CSI) has emerged as a key technology for HAR due to its [...] Read more.
Human activity recognition (HAR) is vital for applications in fields such as smart homes, health monitoring, and navigation, particularly in GNSS-denied environments where satellite signals are obstructed. Wi-Fi channel state information (CSI) has emerged as a key technology for HAR due to its wide coverage, low cost, and non-reliance on wearable devices. However, existing methods face challenges including significant data fluctuations, limited feature extraction capabilities, and difficulties in recognizing complex movements. This study presents a novel solution by integrating a multi-sensor array of Wi-Fi CSI with deep learning techniques to overcome these challenges. We propose a 2 × 2 array of Wi-Fi CSI sensors, which collects synchronized data from all channels within the CSI receivable range, improving data stability and providing reliable positioning in GNSS-denied environments. Using the CNN-LSTM-attention (C-L-A) framework, this method combines short- and long-term motion features, enhancing recognition accuracy. Experimental results show 98.2% accuracy, demonstrating superior recognition performance compared to single Wi-Fi receivers and traditional deep learning models. Our multi-sensor Wi-Fi CSI and deep learning approach significantly improve HAR accuracy, generalization, and adaptability, making it an ideal solution for GNSS-denied environments in applications such as autonomous navigation and smart cities. Full article
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