Improvement of bearings only target tracking using smoothing

Zhang Qian, T. Song
{"title":"Improvement of bearings only target tracking using smoothing","authors":"Zhang Qian, T. Song","doi":"10.1109/CCSSE.2014.7224497","DOIUrl":null,"url":null,"abstract":"Bearings only target tracking is often addressed using linearized estimators such as the extended Kalman filter (EKF). Due to the erratic performance of the EKF algorithm in passive localization, a new filtering method, referred to as the smoothing modified gain EKF (sMGEKF), is proposed based on the modified gain EKF (MGEKF) and Rauch-Tung-Striebel (RTS) smoothing. Compared to the traditional approaches (e.g., the EKF and the iterated EKF) used in passive localization, the proposed method has potential advantages in tracking accuracy. A simulation study demonstrates the advantages of this approach.","PeriodicalId":251022,"journal":{"name":"2014 IEEE International Conference on Control Science and Systems Engineering","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE International Conference on Control Science and Systems Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCSSE.2014.7224497","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

Bearings only target tracking is often addressed using linearized estimators such as the extended Kalman filter (EKF). Due to the erratic performance of the EKF algorithm in passive localization, a new filtering method, referred to as the smoothing modified gain EKF (sMGEKF), is proposed based on the modified gain EKF (MGEKF) and Rauch-Tung-Striebel (RTS) smoothing. Compared to the traditional approaches (e.g., the EKF and the iterated EKF) used in passive localization, the proposed method has potential advantages in tracking accuracy. A simulation study demonstrates the advantages of this approach.
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
利用平滑改进轴承单目标跟踪
只有轴承的目标跟踪通常使用线性化估计器,如扩展卡尔曼滤波器(EKF)。针对EKF算法在被动定位中的不稳定性能,在修正增益EKF (MGEKF)和Rauch-Tung-Striebel (RTS)滤波的基础上,提出了一种新的滤波方法——平滑修正增益EKF (sMGEKF)。与传统的被动定位方法(如EKF和迭代EKF)相比,该方法在跟踪精度上具有潜在的优势。仿真研究表明了该方法的优越性。
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
Early warning model of crisis in Chinese commercial banks based on gray relational analysis with double standard Event detection with vector similarity based on fourier transformation Power load classification based on spectral clustering of dual-scale Particle-beam weapons system The railway turnout fault diagnosis algorithm based on BP neural network
×
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