Polar Motion Ultra-Short-Term Prediction of Least-Squares+Multivariate Autoregressive Hybrid Method by Using the Kalman Filter

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Abstract
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.
Funding Information
  • HuNan Natural Science Fund, China (2024JJ8329, 2023RC3217, 20240105CH, HNGTCH-2023-05, 42004016)
  • science and technology innovation Program of Hunan Province (2024JJ8329, 2023RC3217, 20240105CH, HNGTCH-2023-05, 42004016)
  • Research Foundation of the Department of Natural Resources of Hunan Province (2024JJ8329, 2023RC3217, 20240105CH, HNGTCH-2023-05, 42004016)
  • Hunan Land Surveying and Mapping Project (2024JJ8329, 2023RC3217, 20240105CH, HNGTCH-2023-05, 42004016)
  • National Natural Science Fund, China (2024JJ8329, 2023RC3217, 20240105CH, HNGTCH-2023-05, 42004016)

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