基于AR模型的自适应多位置传感器信息融合方法

Dai Hai-fa, Ma Heng, Bian Hong-wei, Wan Rong-ying
{"title":"基于AR模型的自适应多位置传感器信息融合方法","authors":"Dai Hai-fa, Ma Heng, Bian Hong-wei, Wan Rong-ying","doi":"10.1109/CPGPS.2017.8075088","DOIUrl":null,"url":null,"abstract":"Based on the problem of the difficulty to set up the precise models for the information fuse method which bases on the Kalman filters, a new method based on the autoregression (AR) model is put forward in this paper. This method is one of the time series analysis methods, which uses the temporal correlation between the errors data to set up the AR model, and then the estimation results are utilized to fuse the location information; To detect and isolate the information fault timely, this paper suggests the fault detection and isolation method based on maximum solution separation; Finally, the feasibility of the algorithm is verified by the data collected from the actual sensors.","PeriodicalId":340067,"journal":{"name":"2017 Forum on Cooperative Positioning and Service (CPGPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Adaptive multi-position sensor information fusion method based on AR model\",\"authors\":\"Dai Hai-fa, Ma Heng, Bian Hong-wei, Wan Rong-ying\",\"doi\":\"10.1109/CPGPS.2017.8075088\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on the problem of the difficulty to set up the precise models for the information fuse method which bases on the Kalman filters, a new method based on the autoregression (AR) model is put forward in this paper. This method is one of the time series analysis methods, which uses the temporal correlation between the errors data to set up the AR model, and then the estimation results are utilized to fuse the location information; To detect and isolate the information fault timely, this paper suggests the fault detection and isolation method based on maximum solution separation; Finally, the feasibility of the algorithm is verified by the data collected from the actual sensors.\",\"PeriodicalId\":340067,\"journal\":{\"name\":\"2017 Forum on Cooperative Positioning and Service (CPGPS)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 Forum on Cooperative Positioning and Service (CPGPS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CPGPS.2017.8075088\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Forum on Cooperative Positioning and Service (CPGPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CPGPS.2017.8075088","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

针对基于卡尔曼滤波的信息融合方法难以建立精确模型的问题,提出了一种基于自回归(AR)模型的信息融合方法。该方法是一种时间序列分析方法,利用误差数据之间的时间相关性建立AR模型,然后利用估计结果融合位置信息;为了及时检测和隔离信息故障,本文提出了基于最大解分离的故障检测与隔离方法;最后,通过实际传感器采集的数据验证了算法的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Adaptive multi-position sensor information fusion method based on AR model
Based on the problem of the difficulty to set up the precise models for the information fuse method which bases on the Kalman filters, a new method based on the autoregression (AR) model is put forward in this paper. This method is one of the time series analysis methods, which uses the temporal correlation between the errors data to set up the AR model, and then the estimation results are utilized to fuse the location information; To detect and isolate the information fault timely, this paper suggests the fault detection and isolation method based on maximum solution separation; Finally, the feasibility of the algorithm is verified by the data collected from the actual sensors.
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
期刊最新文献
Research on underwater sound velocity calculation, error correction and positioning algorithms An optimal weighted least squares RAIM algorithm Survey on cyber security of CAV A position self-calibration method in multilateration The application of MEMS GPS receiver in APOD precise orbit determination
×
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