Research on short-term clock bias prediction of BeiDou satellite based on optimized residual difference combination
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摘要: 为解决传统模型因使用卫星钟差一次差分序列而导致预报精度差的问题，进一步提升预报精度，提出一种优化残差组合对卫星钟差一次差分序列进行预报的方法. 该方法首先根据北斗卫星钟差序列的特点，利用四分位法(IQR)代替中位数法对一次差分序列进行预处理，然后利用自回归滑动平均模型(ARMA)将经过预处理后的卫星钟差一次差分序列分成趋势项和残差随机项，接着利用极限学习机(ELM)模型对残差部分进行建模预测，最后将ARMA模型的预测结果和ELM神经网络的残差预测结果求和后进行差分还原. 结果表明：当卫星钟差呈非线性时，组合模型的预报精度比传统模型提升了38.2%，在北斗卫星钟差短期预报中具有一定的可行性.
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关键词:
- 北斗卫星钟差预报 /
- 自回归滑动平均模型(ARMA) /
- 极限学习机(ELM) /
- 组合模型
Abstract: In order to solve the problem of poor prediction accuracy caused by the traditional model using the satellite clock bias primary difference sequence and further improve the prediction accuracy, an optimized residual combination is proposed to forecast the satellite clock bias primary difference sequence. This method firstly according to the characteristics of the beidou satellite clock bias sequence, using quarterback method instead of the median method of time difference sequence preprocessing, and then using autoregressive moving average (ARMA) model after preprocessing the satellite clock bias of a differential sequence is divided into trend item and random item residual, then using the extreme learning machine (ELM) model to simulate the residual part modeling prediction, Finally, the prediction results of ARMA model and residual prediction results of ELM neural network are summed and then differentially restored. The results show that when the satellite clock bias is nonlinear, the prediction accuracy of the combined model is 38.2% higher than that of the traditional model, which has certain feasibility in the short-term prediction of the BeiDou satellite clock bias. -
表 2 不同模型短期预报结果RMSE均值统计
卫星号 预报时长/h 不同模型RMSE统计/ns GM(1,1) ARMA ELM 组合模型 C20 6 4.53 0.20 0.45 0.26 12 7.53 0.18 0.59 0.22 C23 6 0.78 0.12 0.49 0.09 12 1.52 0.24 0.70 0.16 C28 6 1.35 0.21 2.65 0.17 12 3.51 0.47 4.26 0.29 
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[1] 程博, 丘晓枫, 季凌燕, 等. BDS精密卫星钟差建模与预报[J]. 测绘科学, 2019, 44(10): 14-20. [2] 徐维梅, 卢秀山, 郑作亚. 精密单点定位中三种GPS卫星钟差预报模型的精度分析[J]. 测绘信息与工程, 2009, 34(5): 8-10. [3] 陈正生, 吕志平, 张清华, 等. 基于时间序列分解的GPS卫星钟差预报[J]. 测绘科学, 2011, 36(3): 116-118. [4] 王宇谱, 吕志平, 陈正生, 等. 卫星钟差预报的小波神经网络算法研究[J]. 测绘学报, 2013, 42(3): 323-330. [5] 蔡成林, 于洪刚, 韦照川, 等. 基于Takagi-Sugeno模糊神经网络模型的卫星钟差预报方法[J]. 天文学报, 2017, 58(3): 111-124. [6] 王润, 王井利, 吕栋. 导航卫星钟差预报的Elman神经网络算法研究[J]. 大地测量与地球动力学, 2021, 41(3): 285-289,295. DOI: 10.14075/j.jgg.2021.03.012 [7] DING S F, ZHAO H, ZHANG Y N, et al. Extreme learning machine: algorithm, theory and applications[J]. Artificial intelligence review, 2015, 44(1): 103-115. DOI: 10.1007/s10462-013-9405-z [8] 陈晓娟, 郑筱妤, 王圣达, 等. 基于SSA-ELM的光缆故障模式识别方法[J]. 激光杂志, 2022, 43(5): 49-53. DOI: 10.14016/j.cnki.jgzz.2022.05.049 [9] 吕栋, 欧吉坤, 于胜文. 基于MEA-BP神经网络的卫星钟差预报[J]. 测绘学报, 2020, 49(8): 993-1003. DOI: 10.11947/j.AGCS.2020.20200002 [10] 王建敏, 李特, 谢栋平, 等. 北斗精密卫星钟差短期预报研究[J]. 测绘科学, 2020, 45(1): 33-41. [11] 舒颖. GPS坐标时间序列粗差剔除方法比较分析[J]. 导航定位学报, 2021, 9(4): 79-85. DOI: 10.3969/j.issn.2095-4999.2021.04.012 [12] 王博文, 王景升, 朱茵, 等. 基于ARMA-SVR的短时交通流量预测模型研究[J]. 公路交通科技, 2021, 38(11): 126-133. [13] 王建敏, 李特, 吕楠, 等. 差分组合模型在BDS卫星钟差预测中的应用[J]. 导航定位学报, 2022, 10(3): 94-100. DOI: 10.3969/j.issn.2095-4999.2022.03.013 -
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