传感器输入信号的估计,既不受带宽限制也不稀疏

L. Bruderer, Hans-Andrea Loeliger
{"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}
引用次数: 7

摘要

本文研究了以状态空间形式给出的线性传感器连续时间输入信号的估计问题。在之前的工作中,Bolliger等人提出将输入信号建模为(连续时间)高斯白噪声,并推导出基于卡尔曼滤波的相应估计器。本文详细阐述了这一新的估计方法。特别是建立了估计的连续性(或分段连续性),提出了一种新的样本间插值公式,用维纳滤波视角补充了卡尔曼滤波视角,并通过数值实验证明了其实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信