引用本文: 胡超, 王潜心, 王中元, 彭小强. 一种基于观测方程GDOP值的优化选站模型[J]. 武汉大学学报 ( 信息科学版), 2017, 42(6): 838-844.
HU Chao, WANG Qianxin, WANG Zhongyuan, PENG Xiaoqiang. An Optimal Stations Selected Model Based on the GDOP Value of Observation Equation[J]. Geomatics and Information Science of Wuhan University, 2017, 42(6): 838-844.
 Citation: HU Chao, WANG Qianxin, WANG Zhongyuan, PENG Xiaoqiang. An Optimal Stations Selected Model Based on the GDOP Value of Observation Equation[J]. Geomatics and Information Science of Wuhan University, 2017, 42(6): 838-844.

## An Optimal Stations Selected Model Based on the GDOP Value of Observation Equation

• 摘要: 针对GNSS（global navigation satellite system）数据分析中心对快速、超快速轨道产品精度及时效性的要求以及全球跟踪站分布不均匀性的现状，本文提出一种基于观测方程GDOP（geometric dilution of precision）值的优化选站SSS（selected step by step）模型。从理论上推导出精密定轨最小地面跟踪站数与地面最优跟踪站数的计算方法，分别通过s°×s°和k°×k°带全球网格划分，筛选最小跟踪站全球分布，以定轨观测方程GDOP值最小为准则，逐步累加筛选定轨全球跟踪站最优分布。连续6 d的数据分析结果表明，本文提出的优化选站模型，在相同数据处理能力条件下，定轨精度可达整体处理的90%，处理时间缩短50%以上；与一般策略对比表明，SSS模型计算出的轨道精度相当，时间节约20%左右；此模型所选跟踪站为最优或次优，提高了分析中心数据处理效率。

Abstract: With reference to the accuracy and time of rapid and ultra-rapid satellite orbit and the unbalanced distribution of global tracking stations, GNSS Data Analysis Centers are meeting with big challenge. This paper proposes an optimal stations selected model called SSS (selected step by step) which is based on the GDOP (geometric dilution of precision) value of observation equation. Firstly, the calculation methods of optimal and the minimum of ground tracking stations for precise orbit determination were deduced. Secondly, according to the global grid of s°×s°and k°×k°, the distribution of minimum stations were selected out. Thirdly, based on the standard of minimum GDOP, an optimal distribution of global tracking stations was gradually accumulated step by step. Six days continuous experiment shows, on the same numerical computation ability, SSS model can reach 90% level of accuracy as the whole data processing and reduce computation time at less 50%. Comparing with the ordinary methods, it shows that SSS model can get the same accuracy as the ordinary methods, while could save time reaching up 20%. Moreover, several comparison experiments indicated that SSS is the optimal or sub-optimal model for the station selected and improves the efficient of data analysis centers.

/

• 分享
• 用微信扫码二维码

分享至好友和朋友圈