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AI-Enhanced Sensor Fusion for GNSS Positioning Technology and Applications

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 2164

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Special Issue Editors

Dr. Xing Su

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Guest Editor

College of Geodesy and Geomatics, Shandong University of Science and Technology, No. 579 Qianwangang Road, Huangdao District, Qingdao 266590, China
Interests: GNSS; precise orbit determination; LEO PNT
Special Issues, Collections and Topics in MDPI journals

Dr. Guangxing Wang

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Guest Editor

School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
Interests: GNSS; PPP; LEO-enhanced navigation; multi-sensor fusion positioning
Special Issues, Collections and Topics in MDPI journals

Dr. Bin Wang

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Guest Editor

Shanghai Astronomical Observatory, Chinese Academy of Sciences, 80 Nandan Road, Shanghai 200030, China
Interests: GNSS; precise time and frequency transfer; relativistic geodesy
Special Issues, Collections and Topics in MDPI journals

Dr. Xin Xie

E-Mail Website
Guest Editor

GNSS Research Center, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
Interests: satellite navigation; satellite precise orbit determination; LEO PNT

Special Issue Information

Dear Colleague,

Global Navigation Satellite Systems (GNSS) are indispensable for positioning and navigation, yet their performance severely degrades in challenging environments such as urban canyons, dense foliage, and marine areas. Achieving robust and continuous positioning requires intelligent multi-sensor fusion to overcome GNSS limitations.

This Special Issue, "AI-Enhanced Sensor Fusion for GNSS Positioning Technology and Applications," seeks cutting-edge research that addresses the critical challenges of signal blockage, interference, and escalating uncertainties in complex scenarios. We specifically solicit submissions that leverage contemporary artificial intelligence (AI) techniques and innovative sensor fusion methodologies to enhance the reliability, accuracy, and resilience of integrated navigation systems. The focus is on novel approaches that move beyond traditional integration frameworks.

Dr. Xing Su
Dr. Guangxing Wang
Dr. Bin Wang
Dr. Xin Xie
Guest Editors

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Keywords

GNSS
positioning
navigation
sensor fusion
robot positioning

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Published Papers (3 papers)

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26 pages, 24595 KB

Open AccessArticle

Deep Learning-Driven Adaptive-Weight Kalman Filtering for Low-Cost GNSS in Challenging Environments

by Hongxin Zhang, Sizhe Shen, Longjiang Li, Jinglei Zhang, Haobo Li, Dingyi Liu, Zhe Li, Zhiqiang Zhang and Xiaoming Wang

Sensors 2026, 26(9), 2694; https://doi.org/10.3390/s26092694 - 27 Apr 2026

Viewed by 752

Abstract

The quality of Global Navigation Satellite System (GNSS) observations on smartphones is highly susceptible to multipath and non-line-of-sight (NLOS) effects in urban environments, resulting in complex and highly variable observation errors. These challenges highlight the necessity of a reliable stochastic model to ensure robust and unbiased parameter estimation. However, conventional empirical stochastic models, such as elevation-dependent or signal-to-noise ratio (SNR)-based weighting schemes, are often insufficient to capture the rapidly changing stochastic behavior of observations in dense urban environments. To overcome this limitation, an adaptive GNSS stochastic model based on a deep neural network (DNN) is developed by integrating SNR, satellite elevation angle, and post-fit pseudorange residuals, which provide a strong indicator of observation quality and environmental context. Specifically, a fully connected DNN is designed to use SNR, satellite elevation angle, and post-fit pseudorange residual as input features, representing signal strength, satellite geometry, and residual information, respectively, and to learn their nonlinear relationship with measurement uncertainty. The network output is then used to adaptively update the diagonal elements of the measurement noise covariance matrix, thereby realizing epoch-wise adaptive weighting within the Kalman filtering process. The proposed DNN-based stochastic model, together with several conventional models, was evaluated using GNSS observations collected by a low-cost u-blox ZED-F9P receiver (u-blox AG, Thalwil, Switzerland) and a Samsung Galaxy S21+ smartphone (Samsung Electronics Co., Ltd., Suwon, Republic of Korea) during vehicle experiments in dense urban canyons. The code-based single point positioning (SPP) results demonstrate that the DNN-based model consistently outperforms traditional stochastic models under both open-sky and urban conditions. The improvement is particularly pronounced for smartphone observations in severely obstructed environments. The proposed DNN-based model reduces the 3D RMSE from 14.25 m, 13.68 m, and 13.05 m, obtained with the elevation-, SNR-, and integrated elevation–SNR-based models, respectively, to 8.94 m, representing an improvement of approximately 35%. A similar improvement is observed for the u-blox ZED-F9P receiver, where the 3D RMSE decreases from 5.71 m, 4.69 m, and 5.15 m to 3.10 m. These results suggest the effectiveness of the proposed DNN-based stochastic model in mitigating complex observation errors and improving positioning accuracy, providing a promising solution for reliable positioning of low-cost GNSS receivers in challenging urban environments. Full article

(This article belongs to the Special Issue AI-Enhanced Sensor Fusion for GNSS Positioning Technology and Applications)

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19 pages, 2798 KB

Open AccessArticle

A High-Precision Cooperative Localization Method for UAVs Based on Multi-Condition Constraints

by Haiqiao Liu, Wen Jiang, Qing Long, Qijun Xia and Xiang Chen

Sensors 2026, 26(5), 1641; https://doi.org/10.3390/s26051641 - 5 Mar 2026

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Abstract

Global Navigation Satellite Systems (GNSSs) often suffer from significant localization errors in signal-denied environments. Furthermore, the accuracy of multi-UAV cooperative localization is highly sensitive to the relative geometric configuration of the swarm. To address these challenges, this paper proposed a novel high-precision and robust cooperative localization method for UAVs. The proposed method comprised two key modules. First, based on the principle of minimizing the Geometric Dilution of Precision, we optimized both the quantity and geometric configuration of the UAV swarm to identify the top three optimal aerial formations. Second, we introduced Ground-Assisted Reference Stations or Unmanned Ground Vehicles to establish an air–ground cooperative localization system. By leveraging Time Difference of Arrival constraints, this system significantly enhanced localization accuracy and robustness. From this analysis, two optimal hybrid configurations were selected. Experimental results showed that while purely air-based geometric optimization enhanced horizontal coverage, it failed to effectively suppress Z-axis errors due to inadequate vertical baselines, with deviations consistently oscillating between 3.0 m and 5.0 m. Conversely, the introduction of edge-deployed ground reference stations reduced the Position Dilution of Precision to a remarkably low level of 0.75, effectively suppressing error divergence. This demonstrated that the proposed air–ground cooperative scheme outperformed traditional pure air-based swarm approaches in localization performance. These findings hold significant theoretical and practical value. Full article

(This article belongs to the Special Issue AI-Enhanced Sensor Fusion for GNSS Positioning Technology and Applications)

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23 pages, 1844 KB

Open AccessArticle

Short-Term Forecast of Tropospheric Zenith Wet Delay Based on TimesNet

by Xuan Zhao, Shouzhou Gu, Jinzhong Mi, Jianquan Dong, Long Xiao and Bin Chu

Sensors 2026, 26(3), 991; https://doi.org/10.3390/s26030991 - 3 Feb 2026

Viewed by 556

Abstract

The tropospheric zenith wet delay (ZWD) serves as a pivotal parameter for atmospheric water vapour inversion. By converting it into precipitable water vapour, high-temporal-resolution atmospheric humidity monitoring becomes feasible, providing crucial support for enhancing short-term rainfall forecast accuracy. However, ZWD exhibits significant non-stationarity due to complex influencing factors, and traditional models struggle to achieve precise predictions across all scenarios owing to limitations in local feature extraction. This article employs a ZWD prediction method based on the dynamic temporal decomposition module of TimesNet, re-constructing one-dimensional high-frequency ZWD time series into two-dimensional tensors to overcome the technical limitations of conventional models. Comprehensively considering topographical characteristics, climatic features, and seasonal factors, experiments were conducted using 30 s ZWD data from 20 IGS stations. This dataset comprised four consecutive days of PPP solutions for each season in 2023. Through comparative experiments with CNN-ATT and Informer models, the global prediction accuracy, seasonal adaptability, and topographical robustness of TimesNet were systematically evaluated. Results demonstrate that under the input-prediction window configuration where each can achieve the optimal accuracy, TimesNet achieves an average seasonal Root Mean Square Error (RMSE) of 5.73 mm across all seasonal station samples, outperforming Informer (7.89 mm) and CNN-ATT (10.02 mm) by 27.4% and 42.8%, respectively. It maintains robust performance under the most challenging conditions—including summer severe convection, high-altitude terrain, and climatically variable maritime zones—while achieving sub-5 mm precision in stable environments. This provides a reliable algorithmic foundation for short-term precipitation forecasting in Global Navigation Satellite System (GNSS) real-time meteorology. Full article

(This article belongs to the Special Issue AI-Enhanced Sensor Fusion for GNSS Positioning Technology and Applications)

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AI-Enhanced Sensor Fusion for GNSS Positioning Technology and Applications

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