{"title":"Joint estimation of fast-updating state and intermittent-updating state","authors":"Hang Geng, Yan Liang, C. Wen, Yonggang Chen","doi":"10.1109/IConAC.2016.7604886","DOIUrl":null,"url":null,"abstract":"This paper formulates a joint estimation problem of fast-updating state and intermittent-updating state in multi-rate systems. The original multi-rate system is first transformed into a single-rate one. Since the direct use of Kalman filtering method on the lifted system will result in the Kalman smoother, the causality constraints must be taken into account in the filter design. Then, based on the lifted system a multi-rate filter (MRF) with causality constraints is derived in the linear minimum mean squared error (LMMSE) sense using the orthogonality principle. A numerical example is given to show the effectiveness of the proposed filter.","PeriodicalId":375052,"journal":{"name":"2016 22nd International Conference on Automation and Computing (ICAC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 22nd International Conference on Automation and Computing (ICAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IConAC.2016.7604886","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper formulates a joint estimation problem of fast-updating state and intermittent-updating state in multi-rate systems. The original multi-rate system is first transformed into a single-rate one. Since the direct use of Kalman filtering method on the lifted system will result in the Kalman smoother, the causality constraints must be taken into account in the filter design. Then, based on the lifted system a multi-rate filter (MRF) with causality constraints is derived in the linear minimum mean squared error (LMMSE) sense using the orthogonality principle. A numerical example is given to show the effectiveness of the proposed filter.