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GNSS phase observable-specific signal bias (OSB) corrections are essential for widespread application of precise point positioning with ambiguity resolution (PPP-AR) or PPP-RTK. However, subject to the orbital error effects, conventional undifferenced (UD) model-derived BeiDou System (BDS) real-time (RT) OSB products are usually unsatisfactory. In this study, a novel OSB-generating method assisted by the double-differenced (DD) model is proposed. The reliable integer UD ambiguities are obtained by converting DD ambiguities with given ambiguity datums, by which the RT orbit error effects on ambiguity fixing can be reduced during the OSB extraction and PPP-AR process. Validated using data from two regional sparse GNSS reference networks in Shaanxi, China, and Europe, results show that the proposed method-derived OSB products can improve RT PPP-AR performance effectively. In the Shaanxi network, the narrow-lane ambiguity residuals for BDS-3 within ± 0.25 cycles are improved by 23.1% and 33.2% compared to those using the UD model and Centre National d’Etudes Spatiales (CNES)-derived OSB products, respectively, and the corresponding values are 15.2% and 43.1% in the European network. A centimeter- or even millimeter-level positioning accuracy can be achieved for BDS PPP using the poposed OSB products in both networks. In the kinematic PPP-AR test within the Shanxi network, the mean RMS of the BDS-2/3 fixed solutions in the east, north, and up directions is 0.9, 0.7, and 2.3 cm, with a decrease of 57.1%, 53.3%, and 46.5% compared to that using OSB derived by UD model. The median Time-To-First-Fix (TTFF) is also shortened from 23.8 to 7.5 min. [ABSTRACT FROM AUTHOR]

The observation data is the basis for the global navigation satellite system (GNSS) to provide positioning, navigation and timing (PNT) service, and the observation quality directly determines the performance level of the PNT service. At present, the analysis of GNSS observations quality is partial and can only be based on a single index assessment. GNSS observation quality is difficult to analyze comprehensively by fusing multiple indicators. To solve the above problem, the supervised and unsupervised machine learning algorithms are applied, and a new comprehensive and classification method of GNSS observations quality based on the k-means clustering algorithm (k-means) and K-nearest neighbor algorithm (KNN) was proposed. The four core index features of GNSS observations, including data integrity rate, carrier-to-noise-density ratio (CNR), pseudorange multipath and the number of observations per slip, were selected to construct the sample dataset. The sample set was unsupervised clustered based on the k-means algorithm, and the classification label of GNSS observations quality was obtained. Then KNN algorithm was used to construct a comprehensive classification and evaluation model for GNSS observations quality. The data from 30 MGEX stations in the Asia–Pacific region in 2019 were selected for modeling analysis. The experiment results show that: (1) a strong correlation has been revealed between pseudorange multipath, CNR and the number of observations per slip. (2) The average classification correctness rate of the new model was over 90% by n -fold cross-validation. (3) The new model can effectively realize the automatic evaluation and classification of GNSS observations quality and easily distinguish the superiority and inferiority of the station observations. The relevant results provide a new idea for the automatic classification and assessment of GNSS observation quality. [ABSTRACT FROM AUTHOR]

Non-holonomic constraint (NHC) is a commonly used enhancement technology for GNSS/SINS vehicle positioning. Due to the vehicle's inevitable side slip, bounce, and other special movements during driving and the non-ignorable IMU installation angle, the traditional NHC can no longer satisfy the zero-value assumption for vehicle lateral and vertical velocity. The latest research demonstrates that machine learning (ML) can establish a mapping relationship between IMU output and NHC pseudo-measurements and directly predict the NHC value in the observation domain, which can improve the NHC performance. However, the existing ML-based NHC prediction and corresponding stochastic model are not sufficiently accurate and robust, especially for complex vehicle movements, such as turning. Moreover, the current studies mainly focus on IMU-only dead reckon (DR) positioning and lack feasible verification of NHC-assisted GNSS/SINS integrated positioning performance during the complete travel. Thus, we study a convolutional neural network based on a vehicle motion state classification (VMSC) for NHC prediction and an adaptive stochastic model in the variance domain using the NHC innovation residuals. Additionally, we propose an “end-to-end” framework for adaptive NHC-assisted GNSS/SINS vehicle positioning during the whole traveling, and then, the validation experiment is conducted with the vehicle GNSS/SINS tightly coupled real-time kinematic positioning in the urban environment. Numerical results show that the average predicted accuracy of VMSC-based NHC-Nets can reach approximately 2 cm/s, and by using the new NHC, the positioning error is reduced by around 91.4% compared with the IMU-only DR during a 60-s GNSS outage; for the whole traveling, the GNSS/SINS positioning accuracy achieved a significant improvement, especially in the heavily limited GNSS observation environment. Due to a relatively high DR accuracy, the GNSS ambiguity fixed rate and satellite availability can also be promoted to a certain extent. [ABSTRACT FROM AUTHOR]

Due to the multipath and signal shading effects, there exist biases in the carrier phase measurements, which degrades the reliability of ambiguity resolution in real-time kinematic (RTK) positioning and, subsequently, the positioning accuracy. Integrity monitoring is an effective way to measure, improve, and guarantee the correctness of the RTK positioning solutions, but most integrity monitoring has focused only on the pseudorange faults and further the traditional ambiguity validation methods cannot monitor faulty ambiguity resolution separately. To provide high accuracy and high reliability for safety–critical applications, this study proposes a solution separation (SS)-based method for integrity monitoring of RTK precise positioning, in which the SS failure mode and test statistics are used to monitor the reliability of fixed ambiguity solutions by detecting faulty ambiguity fixes and bound positioning errors in the position domain using protection level (PL). The vehicular test with a low-cost receiver shows that the SS method can effectively detect and exclude fault ambiguity resolutions by 98.35%, and the double-difference residuals of SS are reduced from 6.89 cm to 2.56 cm. The positioning accuracy with the SS method has gained an 83.64% improvement compared with the traditional methods. The horizontal PL and the vertical PL can also effectively bound the positioning errors, with average values of 4.49 cm and 12.25 cm, respectively. The SS PL can achieve 99.17% and 96.14% in the horizontal and vertical directions to provide normal integrity service, respectively. [ABSTRACT FROM AUTHOR]