{"title":"用粒子滤波降低非线性系统的感觉误差","authors":"H. Bayram, A. Ertuzun, H. Bozma","doi":"10.1109/SIU.2006.1659715","DOIUrl":null,"url":null,"abstract":"In signal processing and control applications, on-line state estimation plays important role in stability of the system. In cases where state and/or measurement functions are highly nonlinear and/or the noise is not Gaussian, conventional filters such as extended Kalman filters do not provide satisfactory results. In this paper, particle filters and its application to a nonlinear problem are examined","PeriodicalId":415037,"journal":{"name":"2006 IEEE 14th Signal Processing and Communications Applications","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Reduction of Sensory Inaccuracy in Nonlinear Systems using Particle Filters\",\"authors\":\"H. Bayram, A. Ertuzun, H. Bozma\",\"doi\":\"10.1109/SIU.2006.1659715\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In signal processing and control applications, on-line state estimation plays important role in stability of the system. In cases where state and/or measurement functions are highly nonlinear and/or the noise is not Gaussian, conventional filters such as extended Kalman filters do not provide satisfactory results. In this paper, particle filters and its application to a nonlinear problem are examined\",\"PeriodicalId\":415037,\"journal\":{\"name\":\"2006 IEEE 14th Signal Processing and Communications Applications\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-04-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 IEEE 14th Signal Processing and Communications Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIU.2006.1659715\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE 14th Signal Processing and Communications Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIU.2006.1659715","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Reduction of Sensory Inaccuracy in Nonlinear Systems using Particle Filters
In signal processing and control applications, on-line state estimation plays important role in stability of the system. In cases where state and/or measurement functions are highly nonlinear and/or the noise is not Gaussian, conventional filters such as extended Kalman filters do not provide satisfactory results. In this paper, particle filters and its application to a nonlinear problem are examined
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