Low Earth Orbit (LEO) satellites, with their wide coverage and large number, have gradually become a crucial platform and means for navigation and positioning services. Compared to other LEO constellations, Starlink has over 6000 satellites in orbit, and its signals contain synchronous sequences usable for pseudo-range positioning. Thus, it has a potential serve as an alternative to Global Navigation Satellite System (GNSS) for providing Positioning, Navigation, and Timing (PNT) services. Compared to parabolic antennas and electronically scanned phased array antennas for receiving Starlink signals, wide-beam antennas (such as Low-Noise Block (LNB) downconverters) can be used to achieve low-cost multi-satellite tracking. However, it also encounters the issues of distinguishing between multiple satellite signals and low received signal power, which requires high-sensitivity detection. Moreover, there are some other issues such as unknown signal transmission time and real-time satellite orbits, thereby causing failure to navigation. Accordingly, this study focuses on LEO constellations represented by Starlink, proposes a joint pseudo-range and Doppler positioning method for Starlink satellite Signals of Opportunity (SOP) based on the comprehensive utilization of signals and information from LEO satellites. Moreover, this study analyzes the signal detection capability and satellite isolation method, examines the algorithm’s orbital error suppression performance, and establishes the concept of Equivalent Position Dilution of Precision (EPDOP) $$s_{{{\text{EPDOP}}}}$$ s EPDOP for joint pseudo-range and Doppler positioning. In the experiment because Starlink satellite signals are not available in China, we take the advantage of Iridium NEXT satellites’ global seamless coverage, utilize an algorithm to get pseudo-range observations with the Iridium NEXT signals, and then combine them with Starlink Doppler observations to achieve the joint pseudo-range and Doppler positioning of LEO satellites’ SOPs. The experiment results demonstrate that when relying on Starlink Doppler observations for positioning, the proposed method achieves Two-Dimensional (2D) positioning accuracy of 3.6 m and Three-Dimensional (3D) positioning accuracy of 6.2 m. Compared to existing Starlink positioning algorithms based on parabolic antennas and electronically scanned phased array antennas, this method improves the accuracy by at least 35.7%. Additionally, Iridium NEXT signals are used to validate the performance of the proposed algorithm for joint pseudo-range and Doppler positioning. In a 40 km long-baseline scenario, the algorithm achieved a 2D positioning accuracy of 24 m and a 3D positioning accuracy of 41 m with Iridium NEXT satellite pseudo-range and Doppler measurements.

Nowadays, precipitable water vapor (PWV) retrieved from ground-based Global Navigation Satellite Systems (GNSS) tracking stations has heralded a new era of GNSS meteorological applications, especially for severe weather prediction. Among the existing models that use PWV timeseries to predict heavy precipitation, the “threshold-based” models, which are based on a set of predefined thresholds for the predictors used in the model for predictions, are effective in heavy precipitation nowcasting. In previous studies, monthly thresholds have been widely accepted due to the monthly patterns of different predictors being fully considered. However, the primary weakness of this type of thresholds lies in their poor prediction results in the transitional periods between two consecutive months. Therefore, in this study, a new method for the determination of an optimal set of diurnal thresholds by adopting a 31-day sliding window was first proposed. Both the monthly and diurnal variation characteristics of the predictors were taken into consideration in the new method. Then, on the strength of the new method, an improved PWV-based model for heavy precipitation prediction was developed using the optimal set of diurnal thresholds determined based on the hourly PWV and precipitation records for the summer over the period 2010–2017 at the co-located HKSC–KP (King’s Park) stations in Hong Kong. The new model was evaluated by comparing its prediction results against the hourly precipitation records for the summer in 2018 and 2019. It is shown that 96.9% of heavy precipitation events were correctly predicted with a lead time of 4.86 h, and the false alarms resulting from the new model were reduced to 25.3%. These results suggest that the inclusion of the diurnal thresholds can significantly improve the prediction performance of the model.

In recent years, Low Earth Orbit (LEO) satellite Signals of Opportunity (SOP) positioning technology has emerged a significant alternative to the Global Navigation Satellite System (GNSS), effectively addressing navigation and positioning challenges in GNSS-denied environments. Among various LEO constellations, Iridium NEXT has garnered considerable attention due to its well-developed constellation architecture and extensive coverage. To achieve high-precision positioning using Iridium NEXT SOP, it is crucial to fully analyze both time-domain and frequency-domain observation data. Existing time-frequency joint estimation methods for Iridium NEXT signals typically employ a grid-based search strategy, which suffers from low accuracy and high computational complexity. Additionally, the high dynamic of LEO satellites introduces frequency shifts that significantly degrade bit synchronization accuracy and reduce the precision of signal estimation. To address these challenges, this paper proposes a joint high-precision frequency and code phase estimation algorithm. By incorporating a phase adjustment strategy during the bit synchronization stage, it mitigates the impact of satellite dynamics on signal estimation, enhances frequency estimation accuracy, and provides a precise synchronization sequences for code phase estimation. Additionally, leveraging the Early Minus Late Amplitude (EMLA) principle, the algorithm achieves efficient and accurate code phase estimation. It further integrates Frequency of Arrival (FOA) measurements for positioning, ensuring the effective utilization of SOPs to improve positioning accuracy. Experimental signal analysis demonstrates that the proposed algorithm achieves a frequency measurement error of approximately 1Hz, approaching the Cramér-Rao Lower Bound (CRLB). For both long-frame and short-frame signals, the accuracy is 25 m and 37 m, respectively, outperforming existing Time of Arrival (TOA) estimation methods for Iridium NEXT signals. Furthermore, in a 176m baseline differential scenario, Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) fusion positioning accuracy reaches 10m, compared to existing frequency estimation methods, the proposed approach improves FDOA-based positioning accuracy 74%.

The residual-based (RB) receiver autonomous integrity monitoring (RAIM) detector is a widely used receiver integrity enhancement technology that has the ability to rapidly respond to outliers. However, the sensitivity and vulnerability of the residuals to the outliers are the weaknesses of the method especially in the case of multi-outlier modes. It is an effective method for enhancing the validity of residuals by robust estimation instead of least squares (LS) estimation. In this paper, a modified RB RAIM detector based on a robust MM estimation with a higher detection performance under multi-outlier modes is presented. A fast subset selection method based on the characteristic slope that could reduce the number of subsets to be calculated is also presented. The experimental results show that the proposed algorithm maintains a more robust performance than the RB RAIM detector based on the LS estimator and M estimator with an IGG III function especially with the increase in the number of outliers. The proposed fast subset selection method can reduce the calculation time by at least 80%, demonstrating the practical application value of the algorithm.

As an advanced algorithm for Receiver Autonomous Integrity Monitoring (RAIM), Advanced RAIM (ARAIM) has gained considerable attention in the civil aviation sector and is gradually finding applications in other fields. However, with the increasing number of visible satellites, the number of fault subsets processed by the Multiple Hypothesis Solution Separation (MHSS) method grows exponentially, imposing a substantial computational burden on the receiver. Furthermore, ARAIM's uniform distribution of integrity and continuity risks among fault subsets results in overly conservative Protection Levels (PLs).These challenges are often addressed as separate issues. However, this study proposes a novel Protection Level Optimization Algorithm that incorporates a subset grouping method with a feedback structure to reduce the number of fault subsets, thereby decreasing detection time. Additionally, an Improved Cuckoo Search Algorithm (ICSA) is developed to allocate integrity and continuity risks more effectively, optimizing the PLs. Experimental results demonstrate the effectiveness of the proposed method. Compared with ARAIM, the optimization rates of vertical and horizontal PL are improved by 34.38% and 35.06% respectively without inertial navigation; these optimization rates are improved to 74.21% and 74.49% respectively with inertial navigation. In addition, due to the fault subsets reduction, the fault detection time is reduced by 54%, 47% and 26% compared with ARAIM, FPSA and Feedback ARAIM, respectively.

The inter-base station (BS) synchronization error poses a serious challenge to high-precision 5G localization. This letter proposes a joint positioning and synchronization method in an imperfectly-synchronous 5G mmWave system. First, the temporal correlation model of the synchronization error over adjacent epochs is established. Then, the conventional single-epoch Taylor-weighted least squares method is improved and extended to a multi-epoch time of arrival (TOA)-angle of departure (AOD) hybrid positioning scheme. Based on this scheme, a multi-epoch-based joint positioning and synchronization method is proposed, which minimizes state quantities related to the BS clock offsets by employing the temporal correlation model. Finally, the Cramer Rao Lower Bound of positioning error is derived to show the fundamental performance. Results show that the proposed method significantly reduces the positioning error compared with three baseline methods in an imperfectly-synchronous 5G network.