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首页> 外文期刊>Mathematical Problems in Engineering >Random Optimization Algorithm on GNSS Monitoring Stations Selection for Ultra-Rapid Orbit Determination and Real-Time Satellite Clock Offset Estimation
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Random Optimization Algorithm on GNSS Monitoring Stations Selection for Ultra-Rapid Orbit Determination and Real-Time Satellite Clock Offset Estimation

机译：GNSS监测站选择的随机优化算法，用于超快速确定轨道和实时卫星时钟偏移估计

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摘要

Geographical distribution of global navigation satellite system (GNSS) ground monitoring stations affects the accuracy of satellite orbit, earth rotation parameters (ERP), and real-time satellite clock offset determination. The geometric dilution of precision (GDOP) is an important metric used to measure the uniformity of the stations distribution. However, it is difficult to find the optimal configuration with the lowest GDOP when taking the 71% ocean limitation into account, because the ground stations are hardly uniformly distributed on the whole of the Earth surface. The station distribution geometry needs to be optimized and besides the stability and observational quality of the stations should also be taken into account. Based on these considerations, a method of configuring global station tracking networks based on grid control probabilities is proposed to generate optimal configurations that approximately have the minimum GDOP. A random optimization algorithm method is proposed to perform the station selection. It is shown that an optimal subset of the total stations can be obtained in limited iterations by assigning selecting probabilities for the global stations and performing a Monte Carlo sampling. By applying the proposed algorithm for observation data of 201 International GNSS Service (IGS) stations for 3 consecutive days, an experiment of ultra-rapid orbit determination and real-time clock offset estimation is conducted. The distribution effects of stations on the products accuracy are analyzed. It shows that (1) the accuracies of GNSS ultra-rapid observed and predicted orbits and real-time clock offset achieved using the proposed algorithm are higher than those achieved with the traditional method having the drawbacks of lacking evaluation indicators and being time-consuming, corresponding to the improvements 17.15%, 19.30%, and 31.55%, respectively. Only using 30 stations selected by the proposed method, the accuracies achieved reach 2.01 cm (RMS), 4.93 cm (RMS), and 0.20 ns (STD), respectively. Using 60 stations, the accuracies are 1.47 cm, 3.50 cm, and 0.17 ns, respectively. (2) With the increasing number of stations, the accuracies of the Global Positioning System (GPS) orbit and clock offset improve continuously, but more than 60 stations, the improvement on the orbit determination becomes more gradual, while for more than 30 stations, there is no appreciable increase in the accuracy of the real-time clock offset.
机译：全球导航卫星系统（GNSS）地面监测站的地理分布会影响卫星轨道的准确性，地球自转参数（ERP）和实时卫星时钟偏移确定。精度的几何稀释度（GDOP）是用于测量站点分布均匀性的重要指标。但是，考虑到71％的海洋限制，很难找到具有最低GDOP的最佳配置，因为地面站很难在整个地球表面上均匀分布。台站的分布几何需要优化，除了台站的稳定性和观测质量外，还应考虑在内。基于这些考虑，提出了一种基于网格控制概率配置全球站跟踪网络的方法，以生成具有最小GDOP的最佳配置。提出了一种随机优化算法来进行选台。结果表明，通过为全局站点分配选择概率并执行蒙特卡洛采样，可以在有限的迭代中获得全部站点的最佳子集。通过将所提出的算法连续3天应用于201个国际GNSS服务（IGS）站的观测数据，进行了超快速轨道确定和实时时钟偏移估计的实验。分析了工作站对产品精度的分布影响。结果表明：（1）使用该算法所获得的GNSS超快速观测和预测轨道的精度以及实时时钟偏移的准确性高于传统方法所具有的准确性，其缺点是缺乏评估指标，并且比较耗时；分别对应的改进为17.15％，19.30％和31.55％。仅使用通过建议的方法选择的30个站，获得的精度分别达到2.01 cm（RMS），4.93 cm（RMS）和0.20 ns（STD）。使用60个站，精度分别为1.47厘米，3.50厘米和0.17 ns。 （2）随着台站数量的增加，全球定位系统（GPS）轨道和时钟偏移的精度不断提高，但超过60个台站，对轨道确定性的改进变得更加缓慢，而对于30多个台站，实时时钟偏移的精度没有明显增加。

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  • 来源
    《Mathematical Problems in Engineering》 |2019年第4期|7579185.1-7579185.17|共17页
  • 作者

    Yang Xu; Wang Qianxin; Xue Shuqiang;

  • 作者单位

    China Univ Min & Technol, NASG Key Lab Land Environm & Disaster Monitoring, Xuzhou 221116, Jiangsu, Peoples R China|China Univ Min & Technol, Sch Environm Sci & Spatial Informat, Xuzhou 221116, Jiangsu, Peoples R China;

    China Univ Min & Technol, NASG Key Lab Land Environm & Disaster Monitoring, Xuzhou 221116, Jiangsu, Peoples R China|China Univ Min & Technol, Sch Environm Sci & Spatial Informat, Xuzhou 221116, Jiangsu, Peoples R China|RMIT Univ, Satellite Positioning Atmosphere Climate & Enviro, Sch Sci Math & Geospatial Sci, Melbourne, Vic 3001, Australia;

    Chinese Acad Surveying & Mapping, Beijing, Peoples R China;

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