Passive radar target tracking using chirplet transform

F. Farhad Zadeh, H. Amindavar
{"title":"Passive radar target tracking using chirplet transform","authors":"F. Farhad Zadeh, H. Amindavar","doi":"10.1109/SAM.2008.4606916","DOIUrl":null,"url":null,"abstract":"In this paper, we utilize chirplet transformation to estimate the differential delays-Dopplers in an array of sensors. After chirplet modeling of the received signals from each sensor we use extended Kalman filtering (EKF) for tracking the targets by estimating the differential delays and differential Dopplers. This new approach is particularly useful in passive radar and sonar for target tracking. Chirplet modeling is crucial since the received signals are non-stationary in nature.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"340 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SAM.2008.4606916","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

In this paper, we utilize chirplet transformation to estimate the differential delays-Dopplers in an array of sensors. After chirplet modeling of the received signals from each sensor we use extended Kalman filtering (EKF) for tracking the targets by estimating the differential delays and differential Dopplers. This new approach is particularly useful in passive radar and sonar for target tracking. Chirplet modeling is crucial since the received signals are non-stationary in nature.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于小波变换的无源雷达目标跟踪
在本文中,我们利用啁啾变换来估计传感器阵列中的差分延迟-多普勒。在对每个传感器接收到的信号进行啁啾建模后,我们利用扩展卡尔曼滤波(EKF)通过估计差分时延和差分多普勒来跟踪目标。这种新方法在被动雷达和声纳目标跟踪中特别有用。由于接收到的信号本质上是非平稳的,因此啁啾建模是至关重要的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A dual-linear predictor approach to blind source extraction for noisy mixtures Optimal combination of fourth order statistics for non-circular source separation Blind channel identification and signal recovery by confining a component of the observations into a convex-hull of minimum volume Power-aware distributed detection in IR-UWB sensor networks Linear least squares based acoustic source localization utilizing energy measurements
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1