{"title":"基于维纳滤波和卡尔曼滤波的混沌信号降噪研究","authors":"E. Qek, O. Oral, O. Akay","doi":"10.1109/SIU.2007.4298691","DOIUrl":null,"url":null,"abstract":"In this paper, the additive white noise was filtered from chaotic signals obtained by logistic map by using Wiener, extended and unscented Kalman filters, respectively. Performances of each method were compared by finding mean square error versus signal to noise ratio (SNR) and correlation dimension which is one of the invariants of the chaotic dynamical systems. It was observed that, each method exhibits different MSE performances depending on the particular signal to noise ratio.","PeriodicalId":315147,"journal":{"name":"2007 IEEE 15th Signal Processing and Communications Applications","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Noise Reduction in Chaotic Signals by Using Wiener and Kalman Filtering Methods\",\"authors\":\"E. Qek, O. Oral, O. Akay\",\"doi\":\"10.1109/SIU.2007.4298691\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, the additive white noise was filtered from chaotic signals obtained by logistic map by using Wiener, extended and unscented Kalman filters, respectively. Performances of each method were compared by finding mean square error versus signal to noise ratio (SNR) and correlation dimension which is one of the invariants of the chaotic dynamical systems. It was observed that, each method exhibits different MSE performances depending on the particular signal to noise ratio.\",\"PeriodicalId\":315147,\"journal\":{\"name\":\"2007 IEEE 15th Signal Processing and Communications Applications\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-06-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE 15th Signal Processing and Communications Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SIU.2007.4298691\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE 15th Signal Processing and Communications Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIU.2007.4298691","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Noise Reduction in Chaotic Signals by Using Wiener and Kalman Filtering Methods
In this paper, the additive white noise was filtered from chaotic signals obtained by logistic map by using Wiener, extended and unscented Kalman filters, respectively. Performances of each method were compared by finding mean square error versus signal to noise ratio (SNR) and correlation dimension which is one of the invariants of the chaotic dynamical systems. It was observed that, each method exhibits different MSE performances depending on the particular signal to noise ratio.
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