{"title":"最优降阶滤波","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":"{\"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}","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}
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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