{"title":"具有时滞传感器的离散系统的H∞信息融合滤波","authors":"Jie Yan, Xiaohong Lu","doi":"10.1109/CCDC.2009.5192428","DOIUrl":null,"url":null,"abstract":"This paper is concerned with H∞ information fusion filtering for discrete-time systems with time-delay sensors based on re-organization of innovation. The key technique applied for deriving the filter is the ro-organized innovation analysis approach in Krein space. The H∞ filter is given in terms of solutions of Riccati matrix differential equations. It is the first time to present the result of H∞ information fusion for linear discrete-time systems with delayed sensors. In the fusion center, It only requires computing scalar weights, so the calculation burden can be reduced. while this method does not requires that the parameters of the system and the statistics of the noises are assumed to be known exactly. Thus the proposed approach is much more computational attractive.","PeriodicalId":127110,"journal":{"name":"2009 Chinese Control and Decision Conference","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"H∞ information fusion filtering for discrete-time systems with time-delay sensors\",\"authors\":\"Jie Yan, Xiaohong Lu\",\"doi\":\"10.1109/CCDC.2009.5192428\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper is concerned with H∞ information fusion filtering for discrete-time systems with time-delay sensors based on re-organization of innovation. The key technique applied for deriving the filter is the ro-organized innovation analysis approach in Krein space. The H∞ filter is given in terms of solutions of Riccati matrix differential equations. It is the first time to present the result of H∞ information fusion for linear discrete-time systems with delayed sensors. In the fusion center, It only requires computing scalar weights, so the calculation burden can be reduced. while this method does not requires that the parameters of the system and the statistics of the noises are assumed to be known exactly. Thus the proposed approach is much more computational attractive.\",\"PeriodicalId\":127110,\"journal\":{\"name\":\"2009 Chinese Control and Decision Conference\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-06-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 Chinese Control and Decision Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCDC.2009.5192428\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Chinese Control and Decision Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCDC.2009.5192428","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
H∞ information fusion filtering for discrete-time systems with time-delay sensors
This paper is concerned with H∞ information fusion filtering for discrete-time systems with time-delay sensors based on re-organization of innovation. The key technique applied for deriving the filter is the ro-organized innovation analysis approach in Krein space. The H∞ filter is given in terms of solutions of Riccati matrix differential equations. It is the first time to present the result of H∞ information fusion for linear discrete-time systems with delayed sensors. In the fusion center, It only requires computing scalar weights, so the calculation burden can be reduced. while this method does not requires that the parameters of the system and the statistics of the noises are assumed to be known exactly. Thus the proposed approach is much more computational attractive.