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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：用于超快速轨道确定的GNSS监测站选择随机优化算法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影响卫星轨道，地球旋转参数（ERP）和实时卫星时钟偏移测定的准确性。精度（GDOP）的几何稀释是一种重要的指标，用于测量站分布的均匀性。然而，在考虑到71％的海洋限制时，难以找到最低GDOP的最佳配置，因为接地站几乎没有均匀地分布在整个地面表面上。需要优化站分布几何形状，并且除此之外，还应考虑该站的稳定性和观察质量。基于这些考虑，提出了一种基于网格控制概率配置全局站跟踪网络的方法，以产生大致具有最小GDOP的最佳配置。提出了一种随机优化算法方法来执行站选择。结果表明，通过为全局站分配选择概率并执行蒙特卡罗采样，可以在有限的迭代中获得总站的最佳子集。通过将建议的201互国际GNSS服务（IGS）站的观测数据施加到连续3天，进行了超快速轨道测定和实时时钟偏移估计的实验。分析了站点对产品精度的分布效应。它表明（1）使用所提出的算法实现的GNSS超快速观察和预测轨道的精度和实时时钟偏移量高于具有缺乏评估指标的缺点并耗时的传统方法实现的那些。对应于提高17.15％，19.30％和31.55％。仅使用所提出的方法选择的30站，达到达到2.01cm（rms），4.93cm（rms）和0.20ns（std）的精度。使用60个电台，精度分别为1.47厘米，3.50厘米和0.17ns。 （2）随着车站数量越来越多，全球定位系统（GPS）轨道和时钟偏移的准确性连续提高，但超过60个站点，轨道测定的改善变得更加渐进，而超过30站，实时时钟偏移的准确性没有明显的增加。

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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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