R. Cai, Qingxiang Wu, Jiangyong Cai, Jinqing Liu, Meigui Chen
{"title":"无气味卡尔曼滤波在轨道目标跟踪中的简化","authors":"R. Cai, Qingxiang Wu, Jiangyong Cai, Jinqing Liu, Meigui Chen","doi":"10.1109/ITCS.2010.26","DOIUrl":null,"url":null,"abstract":"Aim at the application of orbit object tracking; we adopt certain simplification technology to UKF (Unscented Kalman Filter), which reduce the computational complexity considerably. The state space equation in orbit object tracking is linear; the sigma sampling in unscented transform can be simplified as a composition-add process; the nonlinear transmit of sigma sampling, state vector, measurement vector and their correlation matrix are simplified by an MSUKF (UKF for Mixing system) algorithm. Experiment result shows that, compare with UKF, the proposed algorithm has the same calculation accuracy with considerable lower computational complexity. The amount of computation of proposed algorithm is only 43.33% of that of the UKF.","PeriodicalId":340471,"journal":{"name":"2010 Second International Conference on Information Technology and Computer Science","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Notice of RetractionSimplification of Unscented Kalman Filter for Orbit Object Tracking\",\"authors\":\"R. Cai, Qingxiang Wu, Jiangyong Cai, Jinqing Liu, Meigui Chen\",\"doi\":\"10.1109/ITCS.2010.26\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aim at the application of orbit object tracking; we adopt certain simplification technology to UKF (Unscented Kalman Filter), which reduce the computational complexity considerably. The state space equation in orbit object tracking is linear; the sigma sampling in unscented transform can be simplified as a composition-add process; the nonlinear transmit of sigma sampling, state vector, measurement vector and their correlation matrix are simplified by an MSUKF (UKF for Mixing system) algorithm. Experiment result shows that, compare with UKF, the proposed algorithm has the same calculation accuracy with considerable lower computational complexity. The amount of computation of proposed algorithm is only 43.33% of that of the UKF.\",\"PeriodicalId\":340471,\"journal\":{\"name\":\"2010 Second International Conference on Information Technology and Computer Science\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-07-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 Second International Conference on Information Technology and Computer Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITCS.2010.26\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Second International Conference on Information Technology and Computer Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITCS.2010.26","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Notice of RetractionSimplification of Unscented Kalman Filter for Orbit Object Tracking
Aim at the application of orbit object tracking; we adopt certain simplification technology to UKF (Unscented Kalman Filter), which reduce the computational complexity considerably. The state space equation in orbit object tracking is linear; the sigma sampling in unscented transform can be simplified as a composition-add process; the nonlinear transmit of sigma sampling, state vector, measurement vector and their correlation matrix are simplified by an MSUKF (UKF for Mixing system) algorithm. Experiment result shows that, compare with UKF, the proposed algorithm has the same calculation accuracy with considerable lower computational complexity. The amount of computation of proposed algorithm is only 43.33% of that of the UKF.