Accurate and robust positioning is a critical enabler for Unmanned Aerial Vehicle (UAV) applications, ranging from mapping and inspection to emerging Urban Air Mobility (UAM). While Global Navigation Satellite Systems (GNSS) remain the backbone of absolute positioning, their performance is severely constrained by UAV platform characteristics and complex low-altitude environments. This paper presents a system-level review of GNSS-based UAV positioning. Instead of treating GNSS in isolation, we first link mission requirements and platform constraints, such as aggressive dynamics and Size, Weight, and Power (SWaP) limitations, to specific positioning challenges. We then critically evaluate the spectrum of GNSS techniques, from standalone and Satellite-Based Augmentation System (SBAS) modes to high-precision carrier-phase methods including Real-Time Kinematic (RTK), Post-Processed Kinematic (PPK), Precise Point Positioning (PPP), and PPP-RTK. Furthermore, we discuss multi-sensor fusion with inertial, visual, and Light Detection and Ranging (LiDAR) sensors to mitigate vulnerabilities in urban canyons and GNSS-denied conditions. Finally, we outline key challenges and future directions, highlighting integrity-aware architectures, Artificial Intelligence (AI)-enhanced signal processing, and multi-layer Positioning, Navigation, and Timing (PNT) concepts. The review provides a structured framework and system-level insights to guide resilient navigation for UAV operations in low-altitude airspace.

Utilizing global positioning system (GPS) to determine the precise kinematic orbits for the twin satellites of the Gravity Recovery and Climate Experiment (GRACE) plays a very important role in the earth’s gravitational and other scientific fields. However, the orbit quality is highly depended on the geometry of observed GPS satellites. In this study, we propose a kinematic orbit determination method for improving the GRACE orbit quality especially when the geometry of observed GPS satellites is weak, where an appropriate random walk clock constraint between adjacent epochs is recommended according to the stability of on-board GPS receiver clocks. GRACE data over one month were adopted in the experimental validation. Results show that the proposed method could improve the root mean square (RMS) by 20–40% in radial component and 5–20% in along and cross components. For those epochs with position dilution of precision (PDOP) larger than 4, the orbits were improved by 50–70% in radial component and 17–50% in along and cross components. Meanwhile, the Allan deviation of clock estimates in the proposed method was much closer to the reported Allan deviation of GRACE on-board oscillator. All the results confirmed the improvement of the proposed method.

Accurate rainfall forecasting plays a crucial role in weather monitoring. Currently, the application of Global Navigation Satellite System-derived precipitable water vapor (GNSS-PWV) has mainly focused on forecasting rainfall event occurrence, while neglecting the forecasting of rainfall amount. In this study, a new method based on machine learning fusion strategy and the constraint of rainfall event is proposed. The machine learning fusion strategy is used to improve the accuracy of rainfall amount forecasting by considering the difference in rainfall types and machine learning algorithms, while the rainfall event constraint strategy is used to reduce the rainfall amount forecasting error during periods without rainfall event. In the new method, the Long Short-Term Memory (LSTM) algorithm is adopted to forecast rainfall event by considering the temporal correlation of rainfall event. Then, the Support Vector Regression (SVR), Group Method of Data Handling (GMDH), and Harmony search (HS) algorithms are combined to forecast rainfall amount with the constraint of rainfall event forecast results. The SVR performs better at forecasting little and medium rainfall, while GMDH is better at forecasting heavy rainfall. HS is mainly used to optimize the parameters of the forecasting model. The data sets collected from 2019 to 2023 at Hong Kong GNSS stations are used to evaluate the performance of the proposed method. The experimental results show that after adopting the proposed rainfall event forecasting method, the total True Positive Rate (TPR) is approximately 90.45% and the prediction errors for without rainfall event is only about 4.09%. Moreover, after adopting the proposed rainfall amount forecasting method, the average Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are approximately 0.42 mm and 0.13 mm, respectively. The average improvement rate of MAE is approximately 24.39% for the internal experiment and 25.27% for the external experiment compared to the single SVR model. In conclusion, the proposed method can effectively improve the accuracy of rainfall amount forecast, which can further provide support for meteorological monitoring.

Multi-frequency and multi-GNSS integration is currently becoming an important trend in the development of satellite navigation and positioning technology. In this paper, GPS/Galileo/BeiDou (BDS) precise point positioning (PPP) with ambiguity resolution (AR) are discussed in detail. The mathematical model of triple-system PPP AR and the principle of fractional cycle bias (FCB) estimation are firstly described. With the data of 160 stations in Multi-GNSS Experiment (MGEX) from day of year (DOY) 321-350, 2018, the FCBs of the three systems are estimated and the experimental results show that the range of most GPS wide-lane (WL) FCB is within 0.1 cycles during one month, while that of Galileo WL FCB is 0.05 cycles. For BDS FCB, the classification estimation method is used to estimate the BDS FCB and divide it into GEO and non-GEO (IGSO and MEO) FCB. The variation range of BDS GEO WL FCB can reach 0.5 cycles, while BDS non-GEO WL FCB does not exceed 0.1 cycles within a month. However, the accuracies of GPS, Galileo, and BDS non-GEO narrow-lane (NL) FCB are basically the same. In addition, the number of visible satellites and Position Dilution of Precision (PDOP) values of different combined systems are analyzed and evaluated in this paper. It shows that the triple-system combination can significantly increase the number of observable satellites, optimize the spatial distribution structure of satellites, and is significantly superior to the dual-system and single-system. Finally, the positioning characteristics of single-, dual-, and triple-systems are analyzed. The results of the single station positioning experiment show that the accuracy and convergence speed of the fixed solutions for each system are better than those of the corresponding float solutions. The average root mean squares (RMSs) of the float and the fixed solution in the east and north direction for GPS/Galileo/BDS combined system are the smallest, being 0.92 cm, 0.52 cm and 0.50 cm, 0.46 cm respectively, while the accuracy of the GPS in the up direction is the highest, which is 1.44 cm and 1.27 cm, respectively. Therefore, the combined system can accelerate the convergence speed and greatly enhance the stability of the positioning results.

Tropospheric delay products play a critical role in achieving high-precision positioning in Precise Point Positioning Real-Time Kinematic (PPP-RTK) applications. The short-term prediction of these products remains a significant challenge that warrants further exploration. This study proposes a novel short-term prediction method for tropospheric delay products in PPP-RTK applications, leveraging a multi-scale sliding window and Long Short-Term Memory (LSTM) network. The multi-scale sliding window approach effectively captures data features across different temporal scales, while LSTM, a well-established and robust time series forecasting technique, ensures the accurate modeling of temporal dependencies. The integration of these two methods significantly enhances the precision of short-term tropospheric delay predictions. Experimental analysis utilizing one week of data from the Hong Kong Continuously Operating Reference Stations (CORS) network demonstrates that the proposed method achieves a maximum prediction error of less than 1.5 cm. Furthermore, compared to the standard LSTM approach, the Root Mean Square Error (RMSE) values are improved by 18.9% and 36.6% for different reference values, respectively. PPP-RTK positioning experiments reveal that the predicted products generated by this method exhibit notable improvements in Root Mean Square (RMS) values for the east, north, and up directions, with enhancements of 10.7%, 19.1%, and 4.1%, respectively, over those obtained using the conventional LSTM method. These results comprehensively validate the effectiveness and superiority of the proposed approach.

In deformation monitoring, the severe GNSS multipath caused by reflective surfaces can significantly degrade positioning accuracy. However, traditional multipath mitigation methods often assume strong day-to-day repeatability of residual errors, which is not always valid in complex monitoring environments. We propose a novel GNSS multipath correction approach that leverages multi-day post-fit residual data and principal component analysis to extract stable multipath signals, integrating them into an enhanced spatial repeatability multipath correction model. This method can effectively isolate true multipath errors, even under conditions of weak inter-day repeatability. Experimental results from a dam monitoring network demonstrate that the proposed method reduces the root mean square (RMS) error of single-day kinematic positioning by about 1.8 mm, 2.4 mm, and 6.7 mm in the East, North, and Up components, respectively. For static positioning solutions over 1 h, 2 h, and 4 h sessions, the RMS in East, North, and Up is reduced by approximately 40% on average. After correction, 2 h sessions achieve ~1.1 mm horizontal and ~3.0 mm vertical accuracy, while 4 h sessions reach ~0.9 mm horizontal and ~2.5 mm vertical accuracy. These improvements confirm that the proposed method effectively mitigates multipath effects and meets the high-precision requirements of deformation monitoring.