A Novel Hybrid Approach for UT1-UTC Ultra-Short-Term Prediction Utilizing LOD Series and Sum Series of LOD and First-Order-Difference UT1-UTC
Accurate ultra-short-term prediction of UT1-UTC is crucial for real-time applications in high-precision reference frame conversions. Presently, traditional LS + AR and LS + MAR hybrid methods are commonly employed for UT1-UTC prediction. However, inherent unmodeled errors in fitting residuals of the...
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MDPI AG
2025-02-01
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| author | Fei Ye Minsi Ao Ningbo Li Rong Zeng Xiangqiang Zeng |
| author_facet | Fei Ye Minsi Ao Ningbo Li Rong Zeng Xiangqiang Zeng |
| author_sort | Fei Ye |
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| description | Accurate ultra-short-term prediction of UT1-UTC is crucial for real-time applications in high-precision reference frame conversions. Presently, traditional LS + AR and LS + MAR hybrid methods are commonly employed for UT1-UTC prediction. However, inherent unmodeled errors in fitting residuals of these methods often compromise the prediction performance. Thus, mitigating these common unmodeled errors presents an opportunity to enhance UT1-UTC prediction performance. Consequently, we propose a novel hybrid difference method for UT1-UTC ultra-short-term prediction by integrating LOD prediction and the prediction of the sum of the LOD and the first-order-difference UT1-UTC. The evaluation demonstrated promising results: (1) The mean absolute errors (MAEs) of the proposed method range from 21 to 869 µs in 1–10-day UT1-UTC predictions. (2) Comparative analysis against zero-/first-/second-order-difference LS + AR and zero-/first-order-difference LS + MAR hybrid method reveals a substantial reduction in MAEs by an average of 54/64/44 µs, and 47/20 µs, respectively, with the proposed method. (3) Correspondingly, the proposed method achieves average improvement percentages of 17%/18%/15%, and 13%/3% in 1–10-day UT1-UTC predictions. |
| format | Article |
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| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-ecd5a28b6bbf4d88aea7bc57bf27e52e2025-08-20T02:01:24ZengMDPI AGSensors1424-82202025-02-01254108710.3390/s25041087A Novel Hybrid Approach for UT1-UTC Ultra-Short-Term Prediction Utilizing LOD Series and Sum Series of LOD and First-Order-Difference UT1-UTCFei Ye0Minsi Ao1Ningbo Li2Rong Zeng3Xiangqiang Zeng4BeiDou High-Precision Satellite Navigation and Location Service Hunan Engineering Research Center, Hunan Institute of Geomatics Sciences and Technology, Shaoshanzhong Road No. 693, Changsha 410007, ChinaBeiDou High-Precision Satellite Navigation and Location Service Hunan Engineering Research Center, Hunan Institute of Geomatics Sciences and Technology, Shaoshanzhong Road No. 693, Changsha 410007, ChinaBeiDou High-Precision Satellite Navigation and Location Service Hunan Engineering Research Center, Hunan Institute of Geomatics Sciences and Technology, Shaoshanzhong Road No. 693, Changsha 410007, ChinaBeiDou High-Precision Satellite Navigation and Location Service Hunan Engineering Research Center, Hunan Institute of Geomatics Sciences and Technology, Shaoshanzhong Road No. 693, Changsha 410007, ChinaBeiDou High-Precision Satellite Navigation and Location Service Hunan Engineering Research Center, Hunan Institute of Geomatics Sciences and Technology, Shaoshanzhong Road No. 693, Changsha 410007, ChinaAccurate ultra-short-term prediction of UT1-UTC is crucial for real-time applications in high-precision reference frame conversions. Presently, traditional LS + AR and LS + MAR hybrid methods are commonly employed for UT1-UTC prediction. However, inherent unmodeled errors in fitting residuals of these methods often compromise the prediction performance. Thus, mitigating these common unmodeled errors presents an opportunity to enhance UT1-UTC prediction performance. Consequently, we propose a novel hybrid difference method for UT1-UTC ultra-short-term prediction by integrating LOD prediction and the prediction of the sum of the LOD and the first-order-difference UT1-UTC. The evaluation demonstrated promising results: (1) The mean absolute errors (MAEs) of the proposed method range from 21 to 869 µs in 1–10-day UT1-UTC predictions. (2) Comparative analysis against zero-/first-/second-order-difference LS + AR and zero-/first-order-difference LS + MAR hybrid method reveals a substantial reduction in MAEs by an average of 54/64/44 µs, and 47/20 µs, respectively, with the proposed method. (3) Correspondingly, the proposed method achieves average improvement percentages of 17%/18%/15%, and 13%/3% in 1–10-day UT1-UTC predictions.https://www.mdpi.com/1424-8220/25/4/1087UT1-UTC ultra-short-term predictionnovel hybrid methodLODLS + ARLS + MARsum of LOD and first-order-difference UT1-UTC |
| spellingShingle | Fei Ye Minsi Ao Ningbo Li Rong Zeng Xiangqiang Zeng A Novel Hybrid Approach for UT1-UTC Ultra-Short-Term Prediction Utilizing LOD Series and Sum Series of LOD and First-Order-Difference UT1-UTC Sensors UT1-UTC ultra-short-term prediction novel hybrid method LOD LS + AR LS + MAR sum of LOD and first-order-difference UT1-UTC |
| title | A Novel Hybrid Approach for UT1-UTC Ultra-Short-Term Prediction Utilizing LOD Series and Sum Series of LOD and First-Order-Difference UT1-UTC |
| title_full | A Novel Hybrid Approach for UT1-UTC Ultra-Short-Term Prediction Utilizing LOD Series and Sum Series of LOD and First-Order-Difference UT1-UTC |
| title_fullStr | A Novel Hybrid Approach for UT1-UTC Ultra-Short-Term Prediction Utilizing LOD Series and Sum Series of LOD and First-Order-Difference UT1-UTC |
| title_full_unstemmed | A Novel Hybrid Approach for UT1-UTC Ultra-Short-Term Prediction Utilizing LOD Series and Sum Series of LOD and First-Order-Difference UT1-UTC |
| title_short | A Novel Hybrid Approach for UT1-UTC Ultra-Short-Term Prediction Utilizing LOD Series and Sum Series of LOD and First-Order-Difference UT1-UTC |
| title_sort | novel hybrid approach for ut1 utc ultra short term prediction utilizing lod series and sum series of lod and first order difference ut1 utc |
| topic | UT1-UTC ultra-short-term prediction novel hybrid method LOD LS + AR LS + MAR sum of LOD and first-order-difference UT1-UTC |
| url | https://www.mdpi.com/1424-8220/25/4/1087 |
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