{"title":"一类非线性滤波问题的渐近分析第二部分:一般公式","authors":"G. Blankenship","doi":"10.1109/CDC.1978.267904","DOIUrl":null,"url":null,"abstract":"Generic examples from a class of nonlinear estimation problems involving parameterized Markov processes are formulated so that each is close to a linear, Kalman-Bucy filtering problem for a limited range of the parameter. Based on this approximation, an asymptotic analysis of each problem is outlined starting from the differential equation for the conditional density of the message given the observations.","PeriodicalId":375119,"journal":{"name":"1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Asymptotic analysis of a class of nonlinear filtering problems-Part II: A general formulation\",\"authors\":\"G. Blankenship\",\"doi\":\"10.1109/CDC.1978.267904\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Generic examples from a class of nonlinear estimation problems involving parameterized Markov processes are formulated so that each is close to a linear, Kalman-Bucy filtering problem for a limited range of the parameter. Based on this approximation, an asymptotic analysis of each problem is outlined starting from the differential equation for the conditional density of the message given the observations.\",\"PeriodicalId\":375119,\"journal\":{\"name\":\"1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes\",\"volume\":\"36 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CDC.1978.267904\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CDC.1978.267904","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Asymptotic analysis of a class of nonlinear filtering problems-Part II: A general formulation
Generic examples from a class of nonlinear estimation problems involving parameterized Markov processes are formulated so that each is close to a linear, Kalman-Bucy filtering problem for a limited range of the parameter. Based on this approximation, an asymptotic analysis of each problem is outlined starting from the differential equation for the conditional density of the message given the observations.
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