Search

Usually, the difference between the satellite clocks computed with L1/L2 and clocks computed with L1/L5 is defined as inter-frequency clock bias (IFCB). It is critical to correct its L5 time-variant portion in the GNSS triple-frequency precise positioning. Using two years of observations from more than 100 stations worldwide, we use the epoch-differenced method to estimate IFCB for all available 12 GPS BLOCK-IIF satellites, and analyze its short-term and long-term variations. The experimental results indicate that the IFCB variations are clearly consistent for two satellites located in the same orbital plane, which perhaps means that the variations of IFCB are dependent on the orbital plane. We found that the IFCB of each Block-IIF satellite shows repetition characteristics over two years. The annual repetition cycle of 352 days of IFCB is consistent with the GPS year 351.4 days may originate from the rotation of satellites around the earth. GPS triple-frequency uncombined PPP is carried out using 9 globally distributed MGEX stations from June 1 to 30, 2018. The experimental results indicate that compared to the PPP solutions without IFCB corrections, GPS triple-frequency PPP can achieve an accuracy of 2.2, 3.8 and 11.4 mm in the north, east, and up components after correcting IFCB, which is an accuracy increase in 31.3%, 17.4%, and 13.0%, respectively. The average RMS of the phase posteriori residuals for each frequency is also reduced significantly, especially 79.1% for L5 frequency.

The Global Navigation Satellite System (GNSS) has been an indispensable tool for geodetic surveying and geodynamics research and has rapidly developed over the past few years with abundant ground networks, modern constellations and multiple signal frequencies. However, due to increasing numbers of stations and satellites, the data processing burden has increased significantly. In this contribution, an improved parallel computing method is proposed for processing large GNSS network datasets. First, a parallelization strategy is introduced for the traditional GNSS processing model by analyzing the practicability of parallel integrated processing for GNSS network data. In addition, to maximize the advantages of modern microprocessors and local area network environments, we present the multi-core parallel computing of traditional GNSS data processing methods; then, the product is released as a service-oriented architecture that can be found and invoked through multiple nodes in the Internet. Obviously, this method combines the advantages of multi-core parallelism and network parallelism. Experiments show that the efficiency of the proposed method can be further increased with the accumulation of GNSS data and additional nodes. For example, in a network with 2000 stations, the efficiency of the parallel scheme with four quad-core nodes is at least 8 times faster than that of the traditional serial scheme. All the results demonstrate that the proposed strategy is an efficient and promising approach for processing large GNSS network datasets.

GNSS Data Preprocessor is a multi-GNSS data preprocessing software designed to process raw GNSS observation data in the Receiver Independent Exchange Format 2.x to 3.x standard. Published under a free and open-source license, LGPL (GNU Lesser General Public License), written in object-oriented programming language C#, it mainly includes multi-GNSS file and IGS product automatic acquisition, format conversion, file selection, data visualization, and analysis of GNSS observation files. It provides both a Windows form interface and a command shell interface for the Windows, Linux, or macOS operating system. Volunteers can also participate in and improve the software through GitHub. This software is continuing to evolve, improving its functionalities according to the updates introduced by the collaborators. We give a brief introduction to the software, including software architecture, functions, and modules. Lastly, we give several common examples, including parallel computing, visual satellite graphical display, and cycle slip detection, to show the working status of the current version of the software.

Global navigation satellite system real-time processing requires low latency, high timeliness, and high computational efficiency. A typical application is providing corrections using data from a regional Continuously Operating Reference Station (CORS) network. Usually the wide-lane and narrow-lane fractional cycle biases (FCBs) are determined at the server and broadcast to users to fix undifferenced ambiguity. Also, a tropospheric model is established at the server and broadcast to users to obtain accurate and reliable a priori zenith total delays for precise point positioning (PPP) using the ionospheric-free (IF) observation combination. Currently, serial methods are typically applied, i.e., all reference stations are involved in estimating the wide-lane and narrow-lane FCBs and establishing a regional tropospheric delay model. To improve the efficiency and shorten the latency, we develop a parallel computation method for regional CORS network corrections based on IF PPP by adopting a multicore parallel computing technology task parallel library, wherein parallel computations involving the FCBs, tropospheric delays, and tropospheric model are successively performed based on data parallelism, in which the same operation is performed concurrently on elements in an array, and task parallelism, which refers to one or more independent tasks running concurrently. Data covering four seasons from the Hong Kong and southwestern America CORS networks are utilized in the experiment. The single differenced FCBs between satellites are determined within each full pass, and a tropospheric model with an internal accuracy better than 1.4 cm and an external accuracy better than 1.6 cm is derived at the server. With the parallel implementation, the speedup ratios of FCB estimation and tropospheric modeling are 1.79, 3.15, 5.59, and 9.69 times higher for dual-core, quad-core, octa-core, and hexadeca-core platforms, respectively, than for a single-core platform.

As one of the IGS ultra-rapid predicted (IGU-P) products, the orbit precision has been remarkably improved since late 2007. However, because satellite atomic clocks in space show complicated time---frequency characteristics and are easily influenced by many external factors such as temperature and environment, the IGU-P clock products have not shown sufficient high-quality prediction performance. An improved prediction model is proposed in order to enhance the prediction performance of satellite clock bias (SCB) by employing a wavelet neural network (WNN) model based on the data characteristic of SCB. Specifically, two SCB values of adjacent epoch subtract each other to get the corresponding single difference sequence of SCB, and then, the sequence is preprocessed through using the preprocessing method designed for the single difference sequence. The subsequent step is to model the WNN based on the preprocessed sequence. After the WNN model is determined, the next single difference values at the back of the modeling sequence are predicted. Lastly, the predicted single difference values are restored to the corresponding predicted SCB values. The simulation results have shown that the proposed prediction principle based on the single difference sequence of SCB can make the WNN model simple in architecture and the predicting precision higher than that of the general SCB prediction modeling. The designed preprocessing method specific to the single difference of SCB is able to further improve the prediction performance of the WNN model by reducing the effect from outliers. The proposed SCB prediction model outperforms the IGU-P solutions at least on a daily basis. Specifically, the average prediction precisions for 6, 12 and 24 h based on the proposed model have improved by about 13.53, 31.56 and 49.46 % compared with the IGU-P clock products, and the corresponding average prediction stabilities for 12 and 24 h have increased by about 13.89 and 27.22 %, while the average prediction stability of 6 h is nearly equal.