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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 6429

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Special Issue Editors

Dr. Bobin Cui

E-Mail Website
Guest Editor

1. College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, China
2. Deutsches GeoForschungsZentrum (GFZ), Potsdam, Germany
Interests: multi-GNSS

Dr. Jungang Wang

E-Mail Website
Guest Editor

GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
Interests: space geodetic techniques; global navigation satellite systems; atmospheric delay modeling; precise orbit determination
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Special Issue Information

Dear Colleagues,

The global navigation satellite system (GNSS) arena comprises four primary global systems—GPS, GLONASS, Galileo, and BDS—as well as two regional systems, QZSS and IRNSS. The fusion of these multi-GNSS systems into various devices and services unlocks new opportunities and challenges, necessitating the adoption of advanced methodologies from high-precision and geoscience fields. This integration enhances signal geometry, ensures redundancy, and extends coverage, especially in difficult environments. Algorithmic progress is crucial for leveraging these opportunities and tackling the challenges to improve the precision, availability, interoperability, and integrity of practical GNSS applications. Multi-GNSS is vital for its role in facilitating cutting-edge applications that demand high-precision navigation, such as autonomous vehicles and disaster management, and for maintaining reliable services essential to safety-critical operations. Moreover, it promotes international cooperation, aids in establishing global standards, and propels the evolution of satellite navigation technology, leading to a more interconnected and accurate world.

A Special Issue of the open access journal Remote Sensing (ISSN 2072-4292) is now underway, focusing on ‘Advances in Multi-GNSS Technology and Applications’; it delves into the expanding realm of global navigation satellite systems (GNSSs), presenting both opportunities and challenges in delivering reliable position, navigation, and timing solutions crucial for contemporary human endeavors. We invite submissions covering GNSS receivers, positioning algorithms, contemporary applications, and software tool developments for data collection and processing, along with their applications in diverse fields. This Special Issue seeks to foster dialogue and collaboration in advancing the understanding and utilization of multi-GNSS technology across disciplines.

Contributions may include original research articles, reviews, case studies, and technical notes that provide insights into the current state of the art and future directions in the field of multi-GNSS technology and its diverse applications.

Articles may address, but are not limited, to the following topics:

Multi-GNSS techniques, algorithms, and methodologies;
High-precision GNSS methods;
New methods for atmospheric modeling and applications;
Advances in GNSS signal processing and theoretical modeling;
Multi-sensor applications;
Next-generation signal design for navigation purposes;
GNSS signal processing, positioning, navigation, and timing;
GNSS integrity monitoring, interference mitigation, and novel applications.

Dr. Bobin Cui
Dr. Jungang Wang
Guest Editors

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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

multi-GNSS
multi-sensors
positioning navigation and timing service
GNSS integrity monitoring
GNSS signal processing
multi-technique integration

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Published Papers (8 papers)

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Research

17 pages, 7035 KiB

Open AccessArticle

High-Precision Satellite Clock Offset Estimated by SRIF Based on Epoch-Wise Updated Orbit

by Yu Cao, Le Wang, Zhiwei Qin, Wen Lai, Shi Du and Yuanyuan Wang

Remote Sens. 2025, 17(8), 1391; https://doi.org/10.3390/rs17081391 - 14 Apr 2025

Viewed by 56

Abstract

High-precision clock offset products directly affect the performance and reliability of precise point positioning (PPP) applications. Currently, real-time clock offset products offered by institutions such as the Centre national d’études spatiales (CNES) rely on ultra-rapid predicted orbits. However, these orbits have limited accuracy and exhibit jumps during updates, constraining the accuracy of real-time clock estimation. To address this issue, we propose an undifferenced ambiguity resolution (UD AR) technique for clock offset estimation based on epoch-wise updated orbits. Clock estimation experiments were performed using both predicted and epoch-wise updated orbits, with square root information filtering (SRIF) applied in three schemes: double-differenced (DD), UD, and float solutions. Compared with predicted orbits, epoch-wise updated orbits provided smoother sequences with higher accuracy, significantly improving clock offset estimation accuracy in all schemes. Moreover, the UD AR solution significantly enhanced clock offset estimation accuracy, and the high-precision epoch-wise updated orbit products increased the narrow-lane fixing rate of the UD solutions. The clock accuracies of BDS-3, Galileo, and GPS reached 0.032 ns, 0.023 ns, and 0.026 ns, respectively, representing improvements of 36%, 34%, and 41% compared with the float solutions and 41%, 30%, 26% compared with the UD solution based on 1 h predicted orbits. Finally, the positioning performance of the proposed method was validated via PPP using 25 stations, showing improvements of 50%, 48%, and 41% in the north, east, and up directions compared with CNES products. Therefore, by combining epoch-wise updated orbit products with the UD AR to improve clock accuracy, this method provides a new approach to generating high-precision clock products, significantly contributing to enhancing PPP services. Full article

(This article belongs to the Special Issue Advances in Multi-GNSS Technology and Applications)

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19 pages, 5918 KiB

Open AccessArticle

BeiDou Satellite-Based Augmentation System Algorithm Optimization and Performance Validation of Ionospheric Degradation Parameters with RTCA Protocol

by Zhaochen Li, Yueling Cao, Shanshi Zhou, Xiaogong Hu and Ran Liu

Remote Sens. 2025, 17(7), 1110; https://doi.org/10.3390/rs17071110 - 21 Mar 2025

Viewed by 198

Abstract

The BeiDou Satellite-Based Augmentation System (BDSBAS), based on the Radio Technical Commission for Aeronautics (RTCA) protocol, aims to provide high-precision, single-frequency positioning with integrity assurance for civil aviation users in China and surrounding regions. Given the anticipated high solar activity between 2023 and 2025, ionospheric anomalies may degrade positioning accuracy and significantly impact BDSBAS integrity performance. To enhance BDSBAS integrity, this study evaluates and analyzes the system’s ionospheric degradation parameters for 2023. The results indicate that during the active ionospheric period in 2023, the rate of ionospheric grid delay changes exceeding the limits of the currently broadcasted parameters increased by 0.86%, posing potential integrity risks compared to 2022. To address this issue, we propose a novel algorithm for ionospheric degradation parameters and assess its applicability, stability, and effectiveness using BDSBAS single-frequency service message data from IGS monitoring stations in China. Statistical analysis in the localization domain demonstrates that the new method reduces the rate of ionospheric degradation parameters exceeding the threshold by 1.10% in 2023–2024. This approach significantly enhances BDSBAS integrity service capabilities, supporting its performance improvement and official deployment. Full article

(This article belongs to the Special Issue Advances in Multi-GNSS Technology and Applications)

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24 pages, 3042 KiB

Open AccessArticle

Global Navigation Satellite System Meta-Signals with an Arbitrary Number of Components

by Daniele Borio

Remote Sens. 2025, 17(4), 571; https://doi.org/10.3390/rs17040571 - 7 Feb 2025

Viewed by 464

Abstract

Global Navigation Satellite System (GNSS) meta-signals are obtained when components from different frequencies are jointly processed as a single entity. While most research work has focused on GNSS meta-signals made of two side-band components, meta-signal theory has been recently extended to the case where the number of components is a power of two. This condition was dictated by the use of multicomplex numbers for the representation of GNSS meta-signals. Multicomplex numbers are multi-dimensional extensions of complex numbers whose dimension is a power of two. In this paper, the theory is further extended and a procedure for the construction of GNSS meta-signals with an arbitrary number of components is provided. Also in this case, multicomplex numbers are used to effectively represent a GNSS meta-signal. From this representation, multi-dimensional Cross Ambiguity Functions (CAFs) are obtained and used to derive acquisition and tracking algorithms suitable for the joint processing of components from different frequencies. The specific case with three components is analysed. Theoretical results are supported by experimental findings obtained by jointly processing Galileo E5a, E5b and E6 signals collected using three synchronized Software-Defined Radio (SDR) HackRF One front-ends. Experimental results confirm the validity of the developed theory. Full article

(This article belongs to the Special Issue Advances in Multi-GNSS Technology and Applications)

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21 pages, 4420 KiB

Open AccessArticle

Multi-Layer Perceptron Model Integrating Multi-Head Attention and Gating Mechanism for Global Navigation Satellite System Positioning Error Estimation

by Xiuxun Liu, Zuping Tang and Jiaolong Wei

Remote Sens. 2025, 17(2), 301; https://doi.org/10.3390/rs17020301 - 16 Jan 2025

Cited by 1 | Viewed by 563

Abstract

To better understand and evaluate the GNSS positioning performance, it is convenient to adopt corresponding measures to reduce the impact of errors on positioning. A GNSS positioning error estimation scheme based on an improved multi-layer perceptron model is proposed. The multi-head attention mechanism and gating operation are integrated into the multi-layer perceptron model to adaptively select and filter features, enhancing the model’s ability to understand input features. First, the original positioning error of the satellite is obtained through the Kalman filter positioning method. The data are then preprocessed to extract available features. Finally, the features are input into the constructed model for training and testing to obtain the estimated positioning error value. Two types of comparative experiments were completed. The performance of the presented model is evaluated by the root mean square error. Experimental results show that the proposed method performs well in terms of performance indicators, and has obvious advantages over other state-of-the-art methods. In particular, the root mean square error of the presented method in the first dataset is 0.239 m, which is

39.2 %

and

17 %

lower than the current state-of-the-art long short-term memory network and convolutional neural network, respectively. The presented method can provide higher-precision estimated values for studying the GNSS positioning error estimation problem. Full article

(This article belongs to the Special Issue Advances in Multi-GNSS Technology and Applications)

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24 pages, 19797 KiB

Open AccessArticle

Analysis of Multi-GNSS Multipath for Parameter-Unified Autocorrelation-Based Mitigation and the Impact of Constellation Shifts

by Wenhao Xiong, Yumiao Tian, Xiaolei Dai, Qichao Zhang, Yibing Liang and Xiongwei Ruan

Remote Sens. 2024, 16(21), 4009; https://doi.org/10.3390/rs16214009 - 29 Oct 2024

Cited by 1 | Viewed by 1303

Abstract

Multipath effects can significantly reduce the accuracy of GNSS precise positioning. Traditional methods, such as sidereal filtering and grid-based approaches, attempt to model and mitigate these errors by leveraging the spatial autocorrelation of multipath based on residuals. However, these methods can only approximately handle spatial autocorrelation data, limiting their effectiveness. This study investigates the spatial cross-correlation of residuals between various GNSS frequency bands, analyzes their covariance function parameters, and evaluates the impact of constellation shifts on long-term multipath mitigation. Based on this, a simplified autocorrelation-based approach utilizing unified covariance parameters for multipath mitigation is proposed, with its efficacy assessed for both short- and long-term applications. The study demonstrates the correlation of multipath effects across various GPS and Galileo frequencies, including GPS L1/L2/L5 and Galileo E1/E5a/E5b/E5ab/E6, by analyzing correlation coefficients. A strong correlation (greater than 0.8) is observed between residuals of closely spaced frequencies, such as E5b and E5ab, despite their frequency differences. Additionally, the covariance parameters of the residuals are found to be consistent across all frequencies for a baseline, suggesting that unified parameters can be applied effectively for spatial autocorrelation-based multipath mitigation without sacrificing performance. The orbit shifts of certain GPS satellites, particularly G02, G20, and G21, result in significant changes in orbital parameters and satellite tracks, reducing the effectiveness of long-term multipath mitigation. However, the impact of GPS orbit shifts can be minimized through periodic model updates or by integrating GPS and Galileo modeling. In experiments, the LSC correction strategy based on a GPS/Galileo combination, utilizing unified parameters, outperforms the grid method based on the GPS/Galileo combination, improving the mean residual variance elimination rate by 11.3% for GPS L1 and 11.4% for Galileo E1. These improvements remain consistent, with rates of 11.3% and 15.7%, respectively, even on DOY 365, which is 327 days after the modeling data were collected. Full article

(This article belongs to the Special Issue Advances in Multi-GNSS Technology and Applications)

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18 pages, 4210 KiB

Open AccessArticle

Quantifying Creep on the Laohushan Fault Using Dense Continuous GNSS

by Wenquan Zhuang, Yuhang Li, Ming Hao, Shangwu Song, Baiyun Liu and Lihong Fan

Remote Sens. 2024, 16(19), 3746; https://doi.org/10.3390/rs16193746 - 9 Oct 2024

Viewed by 861

Abstract

The interseismic behavior of faults (whether they are locked or creeping) and their quantitative kinematic constraints are critical for assessing the seismic hazards of faults and their surrounding areas. Currently, the creep of the eastern segment of the Laohushan Fault in the Haiyuan Fault Zone at the northeastern margin of the Tibetan Plateau, as revealed by InSAR observations, lacks confirmation from other observational methods, particularly high-precision GNSS studies. In this study, we utilized nearly seven years of observation data from a dense GNSS continuous monitoring profile (with a minimum station spacing of 2 km) that crosses the eastern segment of the Laohushan Fault. This dataset was integrated with GNSS data from regional continuous stations, such as those from the Crustal Movement Observation Network of China, and multiple campaign measurements to calculate GNSS baseline change time series across the Laohushan Fault and to obtain a high spatial resolution horizontal crustal velocity field for the region. A comprehensive analysis of this primary dataset indicates that the Laohushan Fault is currently experiencing left-lateral creep, characterized by a partially locked shallow segment and a deeper locked segment. The fault creep is predominantly concentrated in the shallow crustal region, within a depth range of 0–5.7 ± 3.4 km, exhibiting a creep rate of 1.5 ± 0.7 mm/yr. Conversely, at depths of 5.7 ± 3.4 km to 16.8 ± 4.2 km, the fault remains locked, with a loading rate of 3.9 ± 1.1 mm/yr. The shallow creep is primarily confined within 3 km on either side of the fault. Over the nearly seven-year observation period, the creep movement within approximately 5 km of the fault’s near field has shown no significant time-dependent variation, instead demonstrating a steady-state behavior. This steady-state creep appears unaffected by postseismic effects from historical large earthquakes in the adjacent region, although the deeper (far-field) tectonic deformation of the Laohushan Fault may have been influenced by the postseismic effects of the 1920 Haiyuan M8.5 earthquake. Full article

(This article belongs to the Special Issue Advances in Multi-GNSS Technology and Applications)

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18 pages, 9061 KiB

Open AccessArticle

A Deep-Learning Based GNSS Scene Recognition Method for Detailed Urban Static Positioning Task via Low-Cost Receivers

by Yubo Li, Zhuojun Jiang, Chuang Qian, Wenjing Huang and Zeen Yang

Remote Sens. 2024, 16(16), 3077; https://doi.org/10.3390/rs16163077 - 21 Aug 2024

Viewed by 1053

Abstract

Global Navigation Satellite Systems (GNSS)-based position service is widely applied in cities, but the precision varies significantly in different obstruction scenes. Scene recognition is critical for developing scene-adaptive GNSS algorithms. However, the complexity of urban environments and the unevenness of received signal especially in low-cost receivers limit the performance of GNSS-based scene recognition models. Therefore, our study aims to construct a scene recognition model suitable for urban static positioning and low-cost GNSS receivers. Firstly, we divide the scenes into five categories according to application requirements, including open area, high urban canyon, unilateral urban canyon, shade of tree and low urban canyon. We then construct feature vectors from original observation data and consider the geometric relationships between satellites and receivers. The different sensitivity to different scenes is discovered through an analysis of the performance of each feature vector in recognition. Therefore, a GNSS positioning scene recognition model based on multi-channel LSTM (MC-LSTM) is proposed. The results of experiments show that an accuracy of 99.14% can be achieved by our model. Meanwhile, only 0.75 s and 1.95 ms are required in model training per epoch and model prediction per data on a CPU, which presents a significant improvement of over 90% compared with existing works. Furthermore, our model can be transferred into different time periods quickly and can maintain robustness in situations where one or two types of observation data are missed. A maximum accuracy of 81.13% can be achieved when two channels are missed, while 96.06% is attainable when one channel is missed. Therefore, our model has the potential for real applications in complex urban environments. Full article

(This article belongs to the Special Issue Advances in Multi-GNSS Technology and Applications)

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18 pages, 6437 KiB

Open AccessArticle

Prediction of Deformation in Expansive Soil Landslides Utilizing AMPSO-SVR

by Zi Chen, Guanwen Huang and Yongzhi Zhang

Remote Sens. 2024, 16(13), 2483; https://doi.org/10.3390/rs16132483 - 6 Jul 2024

Viewed by 924

Abstract

A non-periodic “step-like” variation in displacement is exhibited owing to the repeated instability of expansive soil landslides. The dynamic prediction of deformation for expansive soil landslides has become a challenge in actual engineering for disaster prevention and mitigation. Therefore, a support vector regression prediction (AMPSO-SVR) model based on adaptive mutation particle swarm optimization is proposed, which is suitable for small samples of data. The shallow displacement is decomposed into a trend component and fluctuating component by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the trend displacement is predicted by cubic polynomial fitting. In this paper, the multiple disaster-inducing factors of expansive landslides and the time hysteresis effect between displacement and its influencing factors are fully considered, and the crucial influencing factors which eliminate the time lag effect and state factors are input into the model to predict the fluctuation displacement. Monitoring data in the Ningming area of China are employed for the model validation. The predicted results are compared with those of the traditional model. The model performance is evaluated through indicators such as the goodness of fit R² and root mean square error RMSE. The results show that the prediction RMSE of the new model for three monitoring stations can reach 2.6 mm, 6.6 mm, and 2.5 mm, respectively. Compared with the common Grid search support vector regression (GS-SVR), the Particle Swarm Optimization Support Vector Regression (PSO-SVR) and Back Propagation Neural Network (BPNN) models have average improvements of 58.4%, 38.1%, and 25.2% respectively. The goodness of fit R² is superior to 0.99 in the new method. The proposed model can effectively be deployed for the displacement prediction of non-periodic stepped expansive soil landslides driven by multiple influencing factors, providing a reference idea for the deformation prediction of expansive soil landslides. Full article

(This article belongs to the Special Issue Advances in Multi-GNSS Technology and Applications)

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