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An efficient solution of real-time data processing for multi-GNSS network

机译：多GNSS网络实时数据处理的有效解决方案

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Global navigation satellite systems (GNSS) are acting as an indispensable tool for geodetic research and global monitoring of the Earth, and they have been rapidly developed over the past few years with abundant GNSS networks, modern constellations, and significant improvement in mathematic models of data processing. However, due to the increasing number of satellites and stations, the computational efficiency becomes a key issue and it could hamper the further development of GNSS applications. In this contribution, this problem is overcome from the aspects of both dense linear algebra algorithms and GNSS processing strategy. First, in order to fully explore the power of modern microprocessors, the square root information filter solution based on the blocked QR factorization employing as many matrix-matrix operations as possible is introduced. In addition, the algorithm complexity of GNSS data processing is further decreased by centralizing the carrier-phase observations and ambiguity parameters, as well as performing the real-time ambiguity resolution and elimination. Based on the QR factorization of the simulated matrix, we can conclude that compared to unblocked QR factorization, the blocked QR factorization can greatly improve processing efficiency with a magnitude of nearly two orders on a personal computer with four 3.30 GHz cores. Then, with 82 globally distributed stations, the processing efficiency is further validated in multi-GNSS (GPS/BDS/Galileo) satellite clock estimation. The results suggest that it will take about 31.38 s per epoch for the unblocked method. While, without any loss of accuracy, it only takes 0.50 and 0.31 s for our new algorithm per epoch for float and fixed clock solutions, respectively.
机译：全球导航卫星系统（GNSS）是大地测量研究和对地球的全球监测必不可少的工具，并且在过去的几年中，随着丰富的GNSS网络，现代星座以及数据数学模型的重大改进，它们得到了快速发展。处理。但是，由于卫星和台站数量的增加，计算效率成为一个关键问题，并且可能会阻碍GNSS应用程序的进一步发展。在这一贡献中，从密集线性代数算法和GNSS处理策略方面都克服了这个问题。首先，为了充分利用现代微处理器的功能，介绍了基于块QR分解的平方根信息过滤器解决方案，该解决方案采用了尽可能多的矩阵矩阵运算。另外，通过集中载波相位观测值和模糊度参数，以及执行实时模糊度解析和消除，进一步降低了GNSS数据处理的算法复杂度。基于模拟矩阵的QR分解，我们可以得出结论，与无阻塞QR分解相比，有阻塞QR分解可以在具有四个3.30 GHz内核的个人计算机上以近两个数量级的方式极大地提高处理效率。然后，借助82个全球分布的站点，可以在多GNSS（GPS / BDS /伽利略）卫星时钟估计中进一步验证处理效率。结果表明，对于无阻塞方法，每个时期大约需要31.38 s。同时，在不损失精度的情况下，浮点和固定时钟解决方案的每个时间段的新算法分别只花费0.50和0.31 s。

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