Tan Zhirong, Ye Fei, Hua Liangchun
Bei Dou High-Precision Satellite Navigation and Location Service Hunan Engineering Research Center, Hunan Institue of Geomatics Sciences and Technology, Shaoshanzhong Road No. 693, Changsha 410007, China.
Yi Yang Satellite Application Technology Center, Yi Yang Natural Resources and Planning Bureau, Longzhou South Road No. 299, Yiyang 413000, China.
Sensors (Basel). 2024 Sep 27;24(19):6260. doi: 10.3390/s24196260.
The polar motion (PM, including two parameters PMx and PMy) ultra-short-term prediction (1-10 days) is demanded in the real-time navigation of satellites and spacecrafts. Improving the PMx and PMy ultra-short-term predictions accuracies are a key to optimize the performance of these related applications. Currently, the least squares (LS)+autoregressive (AR) hybrid method is regarded as one of the most capable approaches for ultra-short-term predictions of PMx and PMy. The Kalman filter has proven to be effective in improving the ultra-short-term prediction performance of the LS+AR hybrid method, but the PMx and PMy ultra-short-term predictions accuracies are still not able to satisfy some related applications. In order to improve the performance of PM ultra-short-term prediction, it is worth exploring the combinations of existing methods. Throughout the existing predicted methods, the LS+multivariate autoregressive (MAR) hybrid method by using the Kalman filter has the potential to improve the accuracy of PM ultra-short-term prediction. In addition, a PM prediction performance analysis of the LS+MAR hybrid method by using the Kalman filter, namely the LS+MAR+Kalman hybrid method, is still missing. In this contribution, we proposed the LS+MAR+Kalman hybrid method for PM ultra-short-term prediction. The data sets for PM predictions, which range from 1 to 10 days, have been tested based on the International Earth Rotation and Reference Systems Service Earth Orientation Parameter (IERS EOP) 14 C04 series to assess the performance of the LS+MAR+Kalman hybrid model. The experimental results illustrated that the LS+MAR+Kalman hybrid method can effectively execute PMy ultra-short-term predictions. The improvement of PMy prediction accuracy can rise up to 12.69% for 10-day predictions, and the improvement of ultra-short-term predictions is 7.64% on average.
在卫星和航天器的实时导航中，需要对极移（PM，包括两个参数PMx和PMy）进行超短期预测（1 - 10天）。提高PMx和PMy的超短期预测精度是优化这些相关应用性能的关键。目前，最小二乘法（LS）+自回归（AR）混合方法被认为是对PMx和PMy进行超短期预测最有效的方法之一。卡尔曼滤波器已被证明在提高LS + AR混合方法的超短期预测性能方面是有效的，但PMx和PMy的超短期预测精度仍无法满足一些相关应用的需求。为了提高PM超短期预测的性能，探索现有方法的组合是值得的。在现有的预测方法中，使用卡尔曼滤波器的LS +多元自回归（MAR）混合方法有提高PM超短期预测精度的潜力。此外，目前还缺少对使用卡尔曼滤波器的LS + MAR混合方法（即LS + MAR +卡尔曼混合方法）的PM预测性能分析。在本研究中，我们提出了用于PM超短期预测的LS + MAR +卡尔曼混合方法。基于国际地球自转和参考系统服务地球定向参数（IERS EOP）14 C04系列，对1至10天的PM预测数据集进行了测试，以评估LS + MAR +卡尔曼混合模型的性能。实验结果表明，LS + MAR +卡尔曼混合方法能够有效地进行PMy超短期预测。对于10天的预测，PMy预测精度的提高可达12.69%，超短期预测的平均提高率为7.64%。
