{"title":"基于残差分离和局部化的卡尔曼滤波改进","authors":"Il’mir R. Gogorev, Grigorij V. Belsky","doi":"10.1109/scm55405.2022.9794847","DOIUrl":null,"url":null,"abstract":"A modification of the Kalman filter with residual separation and localization is proposed, which makes it possible to develop an estimate of the measured and restored variables affected by noise under conditions of inaccurately known noise intensities and parametric uncertainty of the plant model. The results of mathematical modeling are presented, demonstrating the advantages of the proposed modification over the classical approach to constructing a Kalman filter.","PeriodicalId":162457,"journal":{"name":"2022 XXV International Conference on Soft Computing and Measurements (SCM)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modification of the Kalman Filter with Residual Separation and Localization\",\"authors\":\"Il’mir R. Gogorev, Grigorij V. Belsky\",\"doi\":\"10.1109/scm55405.2022.9794847\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A modification of the Kalman filter with residual separation and localization is proposed, which makes it possible to develop an estimate of the measured and restored variables affected by noise under conditions of inaccurately known noise intensities and parametric uncertainty of the plant model. The results of mathematical modeling are presented, demonstrating the advantages of the proposed modification over the classical approach to constructing a Kalman filter.\",\"PeriodicalId\":162457,\"journal\":{\"name\":\"2022 XXV International Conference on Soft Computing and Measurements (SCM)\",\"volume\":\"105 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 XXV International Conference on Soft Computing and Measurements (SCM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/scm55405.2022.9794847\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 XXV International Conference on Soft Computing and Measurements (SCM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/scm55405.2022.9794847","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Modification of the Kalman Filter with Residual Separation and Localization
A modification of the Kalman filter with residual separation and localization is proposed, which makes it possible to develop an estimate of the measured and restored variables affected by noise under conditions of inaccurately known noise intensities and parametric uncertainty of the plant model. The results of mathematical modeling are presented, demonstrating the advantages of the proposed modification over the classical approach to constructing a Kalman filter.
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