{"title":"非线性系统的参数估计:自适应创新模型滤波器与自适应扩展卡尔曼滤波器","authors":"C. Bohn","doi":"10.1109/ICIT.2000.854232","DOIUrl":null,"url":null,"abstract":"The problem of recursively estimating the states and parameters of a nonlinear continuous-time system with discrete measurements is investigated. As a new method, an adaptive extended Kalman filter is proposed and compared to an existing approach, an innovations model filter. By means of a simulation example, it is illustrated that both methods are capable of estimating the parameters of a nonlinear system, but that due to the time-varying filter gain in the new method, better state estimates are obtained. The new method is therefore considered a valuable alternative to existing methods.","PeriodicalId":405648,"journal":{"name":"Proceedings of IEEE International Conference on Industrial Technology 2000 (IEEE Cat. No.00TH8482)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Parameter estimation for nonlinear systems: adaptive innovations model filters vs. adaptive extended Kalman filters\",\"authors\":\"C. Bohn\",\"doi\":\"10.1109/ICIT.2000.854232\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The problem of recursively estimating the states and parameters of a nonlinear continuous-time system with discrete measurements is investigated. As a new method, an adaptive extended Kalman filter is proposed and compared to an existing approach, an innovations model filter. By means of a simulation example, it is illustrated that both methods are capable of estimating the parameters of a nonlinear system, but that due to the time-varying filter gain in the new method, better state estimates are obtained. The new method is therefore considered a valuable alternative to existing methods.\",\"PeriodicalId\":405648,\"journal\":{\"name\":\"Proceedings of IEEE International Conference on Industrial Technology 2000 (IEEE Cat. No.00TH8482)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-01-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of IEEE International Conference on Industrial Technology 2000 (IEEE Cat. No.00TH8482)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIT.2000.854232\",\"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 IEEE International Conference on Industrial Technology 2000 (IEEE Cat. No.00TH8482)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIT.2000.854232","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Parameter estimation for nonlinear systems: adaptive innovations model filters vs. adaptive extended Kalman filters
The problem of recursively estimating the states and parameters of a nonlinear continuous-time system with discrete measurements is investigated. As a new method, an adaptive extended Kalman filter is proposed and compared to an existing approach, an innovations model filter. By means of a simulation example, it is illustrated that both methods are capable of estimating the parameters of a nonlinear system, but that due to the time-varying filter gain in the new method, better state estimates are obtained. The new method is therefore considered a valuable alternative to existing methods.