{"title":"Optimal reduced-order filtering","authors":"L. Hong","doi":"10.1109/NAECON.1991.165782","DOIUrl":null,"url":null,"abstract":"An optimal reduced-order filter is developed which can provide a full vector of state estimates for the case where the dimension of the measurement vector is smaller than that of the state vector and no measurements are noise-free. The reduced-order filter consists of an observer type subfilter and a complementary subfilter, each of which provides a subset of the optimal estimate. A two-step L-K transformation is employed to minimize the estimate error covariance of each subfilter. A target tracking problem is studied as an example.<<ETX>>","PeriodicalId":247766,"journal":{"name":"Proceedings of the IEEE 1991 National Aerospace and Electronics Conference NAECON 1991","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1991-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the IEEE 1991 National Aerospace and Electronics Conference NAECON 1991","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NAECON.1991.165782","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
An optimal reduced-order filter is developed which can provide a full vector of state estimates for the case where the dimension of the measurement vector is smaller than that of the state vector and no measurements are noise-free. The reduced-order filter consists of an observer type subfilter and a complementary subfilter, each of which provides a subset of the optimal estimate. A two-step L-K transformation is employed to minimize the estimate error covariance of each subfilter. A target tracking problem is studied as an example.<>
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