GPS/BDS short-term ISB modelling and prediction
GPS/BDS short-term ISB modelling and prediction
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DOI：
10.1007/s10291-015-0513-x
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发表时间：
2016
期刊：
影响因子：
4.9
通讯作者：
Nan Jiang;Yan Xu;Tianhe Xu;Guochang Xu;Zhangzhen Sun;H. Schuh
中科院分区：
文献类型：
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作者：
Nan Jiang;Yan Xu;Tianhe Xu;Guochang Xu;Zhangzhen Sun;H. Schuh
中国北斗导航卫星系统（BDS）自2012年12月27日起完成了其第一个里程碑，提供覆盖亚太地区的导航服务。结合北斗系统，GNSS精密单点定位（PPP）可以提高其定位精度、可用性和可靠性。然而，为了实现最佳定位解决方案，必须尽可能精确地解决GPS和BDS之间的系统间偏差。在这项研究中，一个为期一周（GPS周1810）的GPS/BDS观测18个分布式站的国际GNSS服务多GNSS实验进行了处理。首先，每30分钟通过扩展卡尔曼滤波器作为分段参数来估计ISB。然后，我们生成一个平滑的ISB系列（ISB_s）与滑动窗口中值滤波器，以拒绝从原始估计的ISB系列（ISB_o）的离群值。本文在分析了ISB_s的特点后，提出了一个基于1周周期的短期台站相关ISB模型。该模型由一个时间上的二次多项式和两个或三个具有昼夜和半日周期的周期函数组成。频谱分析用于确定周期函数的周期，二次函数和周期函数的系数由最小二乘估计。对于模型验证，我们将从模型导出的ISB（ISB_m）与ISB_s（假设真值）进行比较。比较结果显示几乎呈正态分布。结果表明，该模型与实测值吻合较好，均方根（RMS）值约为0.7 ns，有些台站甚至更好。这意味着所提出的短期ISB模型具有较高的拟合精度。因此，它可以用于ISB预测。将预报的ISB序列（ISB_p）与下一周（GPS第1811周）的ISB_s进行比较，可以得出预报精度随时间的增加而下降的结论。1天周期预报精度可达0.57-1.21 ns，而2天周期预报精度下降到0.77-1.72 ns。因此，我们建议预测持续时间为1天。该模型将有利于后续的GPS/BDS PPP或精密定轨（POD），因为从这个模型推导出的ISB可以被认为是一个先验约束的PPP/POD的解决方案。在此先验约束下，N、E和U分量的收敛时间分别缩短了19.6%、16.1%和2.4%。E分量的结果精度显著提高了11.9%。
The Chinese BeiDou Navigation Satellite System (BDS) has completed its first milestone by providing coverage of the Asia–Pacific area navigation service since December 27, 2012. With the combination of BDS, the GNSS precise point positioning (PPP) can improve its positioning accuracy, availability and reliability. However, in order to achieve the best positioning solutions, the inter-system bias (ISB) between GPS and BDS must be resolved as precisely as possible. In this study, a 1-week period (GPS week 1810) of GPS/BDS observations for 18 distributed stations from the International GNSS Service Multi-GNSS Experiment are processed. Primarily, the ISB is estimated by an extended Kalman filter as a piece-wise parameter every 30 min. Then we generate a smoothed ISB series (ISB_s) with a sliding window median filter to reject the outliers from the original estimated ISB series (ISB_o). After analysing the characteristics of the ISB_s, a short-term station-dependent ISB model based on a 1-week period is proposed in this study. This model consists of a quadratic polynomial in time and two or three periodic functions with diurnal and semi-diurnal periods. Frequency spectrum analysis is used to determine the periods of the periodic functions, and the coefficients of the quadratic function and the periodic functions are estimated by least squares. For model verification, we compare the ISB derived from the model (ISB_m) with ISB_s (assumed the true values). The comparisons indicate an almost normal distribution. It is found that the proposed model is consistent with the true values: the root-mean-square (RMS) values being about 0.7 ns, and some stations are even better. This means that the short-term ISB model proposed has a high fitting accuracy. Hence, it can be used for ISB prediction. Comparing the prediction ISB series (ISB_p) with ISB_s in the following week (GPS week 1811), we can draw the conclusion that the accuracy of the prediction declines with an increase in the time period. The 1-day period precision can achieve 0.57–1.21 ns, while the accuracy of the 2-day prediction decreases to 0.77–1.72 ns. Hence, we recommend a predicting duration of 1 day. The proposed model will be beneficial for subsequent GPS/BDS PPP or precise orbit determination (POD) since the ISB derived from this model can be considered as a priori constraint in the PPP/POD solutions. With this a priori constraint, the convergence time can be shortened by 19.6, 16.1 and 2.4 % in N, E and U components, respectively. The accuracy of result in the E component is remarkably improved by 11.9 %.