{"title":"传感器输入信号的估计,既不受带宽限制也不稀疏","authors":"L. Bruderer, Hans-Andrea Loeliger","doi":"10.1109/ITA.2014.6804232","DOIUrl":null,"url":null,"abstract":"The paper addresses the estimation of the continuous-time input signal to a linear sensor that is given in state-space form. In previous work, Bolliger et al. proposed to model the input signal as (continuous-time) white Gaussian noise and derived a corresponding estimator that is based on Kalman filtering. The present paper elaborates on this new estimator. In particular, it establishes the continuity (or the piecewise continuity) of the estimate, presents a new interpolation formula between samples, complements the Kalman-filter perspective by a Wiener-filter perspective, and demonstrates practicality by numerical experiments.","PeriodicalId":338302,"journal":{"name":"2014 Information Theory and Applications Workshop (ITA)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Estimation of sensor input signals that are neither bandlimited nor sparse\",\"authors\":\"L. Bruderer, Hans-Andrea Loeliger\",\"doi\":\"10.1109/ITA.2014.6804232\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper addresses the estimation of the continuous-time input signal to a linear sensor that is given in state-space form. In previous work, Bolliger et al. proposed to model the input signal as (continuous-time) white Gaussian noise and derived a corresponding estimator that is based on Kalman filtering. The present paper elaborates on this new estimator. In particular, it establishes the continuity (or the piecewise continuity) of the estimate, presents a new interpolation formula between samples, complements the Kalman-filter perspective by a Wiener-filter perspective, and demonstrates practicality by numerical experiments.\",\"PeriodicalId\":338302,\"journal\":{\"name\":\"2014 Information Theory and Applications Workshop (ITA)\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-04-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 Information Theory and Applications Workshop (ITA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITA.2014.6804232\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 Information Theory and Applications Workshop (ITA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITA.2014.6804232","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Estimation of sensor input signals that are neither bandlimited nor sparse
The paper addresses the estimation of the continuous-time input signal to a linear sensor that is given in state-space form. In previous work, Bolliger et al. proposed to model the input signal as (continuous-time) white Gaussian noise and derived a corresponding estimator that is based on Kalman filtering. The present paper elaborates on this new estimator. In particular, it establishes the continuity (or the piecewise continuity) of the estimate, presents a new interpolation formula between samples, complements the Kalman-filter perspective by a Wiener-filter perspective, and demonstrates practicality by numerical experiments.