GPS Solutions

The Galileo High Accuracy Service (HAS) has undergone substantial development in recent years, offering users free access to GPS and Galileo satellite orbit, clock, and code bias corrections for Precise Point Positioning (PPP) on a global scale. This paper explores the use of the currently available HAS corrections for smartphone positioning. Due to hardware disparities and limited tracking capabilities, smartphone processing with only two GNSS constellations struggles to ensure satisfactory satellite geometry and sufficient observations in realistic user environments. To fully harness all observed measurements and the orbit and clock information directly disseminated from satellites, this study introduces a new PPP algorithm combing HAS corrections and broadcast ephemerides (HAS and BRDC PPP) for smartphone processing. Through four vehicle experiments in urban environments, the proposed HASandBRDC PPP solutions demonstrated a notable reduction in positioning errors. Specifically, the horizontal rms and 95th percentile error decreased from 2.0 and 3.3 m to 1.6 and 2.4 m, respectively, when compared to the HAS PPP solutions. These results are highly comparable to four-constellation PPP solutions utilizing Centre National d'Etudes Spatiales (CNES) ultra-rapid products, which can achieve a horizontal rms of 1.4 m. Additionally, the inclusion of smartphone inertial measurement unit (IMU) measurements results in a notable 59% average reduction in PPP gross errors. This study provides an original comparison of the 2022 and 2023 HAS corrections, demonstrating the feasibility of real-time lane-level navigation with smart devices even in remote areas without Internet connectivity, which has not been previously explored.

The School of Geodesy and Geomatics at Wuhan University has developed a GNSS/SINS post-processing service system named POSMind. With the growing demand for mobile mapping in scientific and engineering applications, such as earth observation and high-precision mapping, there is a crucial need for efficient and accurate direct georeference based on GNSS/SINS integration. To accommodate diverse applications, an analysis of existing service forms was conducted, culminating the development of the hierarchical post-processing service system. This system consists of three service forms: module for interface calls, software for fine processing, and web for efficient cluster processing. POSMind has assimilated existing excellent methodologies and constructed a high-precision GNSS/SINS integration algorithm framework through theoretical derivations and experimental tests. Refinements have been introduced in several facets, including pre-processing, quality control, ambiguity resolution, and smoothing schemes. To assess the performance of POSMind, a series of experiments and analyses were conducted. The first experiment is conducted in open-sky environments (including carborne, airborne, and shipborne) to evaluate the consistency between POSMind and Inertial Explorer. Additionally, experiment under urban environments is carried out to assess the performance of POSMind in realistic cases. Moreover, the practical performance of POSMind was also demonstrated with two mobile mapping cases, with the evaluation of the accuracy of point cloud. Looking forward, we plan to enhance POSMind by introducing reliable filters or optimizers, integrating observations from other sensors and utilizing the benefits of post-processing in existing powerful GNSS/SINS processing platforms. The goal is to provide a powerful GNSS/SINS post-processing service that delivers high-precision, excellent availability, and utmost reliability for diverse scenes and applications. The POSMind web and software can be freely accessed at posmind-web.com and on Kaggle website at kaggle.com/datasets/fengzhusgg/smartpnt-pos.

The regional ionospheric corrections have become one of the critical parts of Precise Point Positioning (PPP)-based Real-Time-Kinematic (RTK) services to achieve fast positioning convergence. Several methods for regional ionospheric corrections supporting PPP-RTK have been developed and implemented over recent years, but little attention is given to the theoretical foundation of existing ionospheric correction methods and their performance comparison to find an optimal method in some sense. The optimality criterion of such methods should not only be based on the precision of the ionospheric correction itself, but also on its broadcasting strategies, and implementation aspects. This contribution studies ionospheric correction generation methods within the best linear unbiased predictor (BLUP) framework. Comparing the accuracy performances of the methods, we demonstrate that the Kriging method with trend, as a special case of BLUP, is the most appropriate method for large-scale networks (above 500 km). A strategy for the evaluation of the uncertainty of the grid-interpolated ionospheric corrections is also developed. In contrast to other empirical methods, this new method is rigorous in the sense that it avoids the underestimation of the uncertainty of predicted ionospheric corrections, especially when reference stations are close to a grid point.

Global Navigation Satellite System (GNSS) data preprocessing is crucial for achieving high-precision navigation, positioning, and timing applications to convert the data format, edit the data content, and analyze the data quality. However, existing preprocessing tools are not yet user-friendly enough to handle multi-frequency and multi-system GNSS data due to extra costs, strong professionalism, and complex operations. In this paper, we present an open-source GNSS Data Preprocessing Software (GDPS) written by Python. It compiles and runs on both Windows and Linux operating systems, supports processing Receiver Independent Exchange Format (RINEX) 2.11 to 4.01 format data, and implements four functional modules: format translation, data editing, quality checking, and auxiliary tools. In addition, it also offers a user-friendly graphical user interface (GUI) that enables users to customize and select different processing setting parameters and then analyze the corresponding results through drawing tools. This paper takes the WUH2 station derived from the International GNSS Service (IGS) as an example to evaluate software performance. The results indicate that GDPS software can meet multi-frequency and multi-GNSS data preprocessing requirements and is expected to provide an open-source, convenient, and integrated GNSS data preprocessing solution for relevant researchers.

We distinguish between geocenter location and geocenter motion for their different purposes: precise orbit determination (POD) and earth surface mass variation monitoring. We present a method to measure the geocenter location through GPS tracked low earth orbiters (LEO) POD. We propose two methods to apply these geocenter location estimates to earth surface mass variation study. 19 years of daily geocenter locations in IGS14 reference frame are estimated from the POD of GPS tracked LEOs. These estimates are applied to 296 globally distributed GPS ground stations measured in the same reference frame for surface mass variation inversion. The results show that such determined geocenter location reaches sub-millimeter precision in each component judged by RMS of the overlapping differences. The geocenter motion change measured using the 80-station GPS orbit and clock product shows similar annual variation to the degree-1 term of the surface mass variation spherical harmonics determined from the 296-station inversion.