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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.<\/jats:p>","DOI":"10.3390\/s24196260","type":"journal-article","created":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T07:49:24Z","timestamp":1727423364000},"page":"6260","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Polar Motion Ultra-Short-Term Prediction of Least-Squares+Multivariate Autoregressive Hybrid Method by Using the Kalman Filter"],"prefix":"10.3390","volume":"24","author":[{"given":"Zhirong","family":"Tan","sequence":"first","affiliation":[{"name":"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"},{"name":"Yi Yang Satellite Application Technology Center, Yi Yang Natural Resources and Planning Bureau, Longzhou South Road No. 299, Yiyang 413000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Ye","sequence":"additional","affiliation":[{"name":"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"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liangchun","family":"Hua","sequence":"additional","affiliation":[{"name":"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"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1016\/j.asr.2015.11.006","article-title":"Influence of ITRS\/GCRS implementation for astrodynamics: Coordinate transformations","volume":"57","author":"Bradley","year":"2016","journal-title":"Adv. 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