基于Dirichlet过程混合的GNSS伪距误差密度跟踪

N. Viandier, J. Marais, A. Rabaoui, E. Duflos
{"title":"基于Dirichlet过程混合的GNSS伪距误差密度跟踪","authors":"N. Viandier, J. Marais, A. Rabaoui, E. Duflos","doi":"10.1109/ICIF.2010.5711829","DOIUrl":null,"url":null,"abstract":"In satellite navigation system, classical localization algorithms assume that the observation noise is white-Gaussian. This assumption is not correct when the signal is reflected on the surrounding obstacles. That leads to a decrease of accuracy and of continuity of service. To enhance the localization performances, a better observation noise density can be use in an adapted filtering process. This article aims to show how the Dirich-let Process Mixture can be employed to track the observation density on-line. This sequential estimation solution is adapted when the noise is non-stationary. The approach will be tested under a simulation scenario with multiple propagation conditions. Then, this density modeling will be used in Rao-Blackwellised Particle Filter.","PeriodicalId":341446,"journal":{"name":"2010 13th International Conference on Information Fusion","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"GNSS pseudorange error density tracking using Dirichlet Process Mixture\",\"authors\":\"N. Viandier, J. Marais, A. Rabaoui, E. Duflos\",\"doi\":\"10.1109/ICIF.2010.5711829\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In satellite navigation system, classical localization algorithms assume that the observation noise is white-Gaussian. This assumption is not correct when the signal is reflected on the surrounding obstacles. That leads to a decrease of accuracy and of continuity of service. To enhance the localization performances, a better observation noise density can be use in an adapted filtering process. This article aims to show how the Dirich-let Process Mixture can be employed to track the observation density on-line. This sequential estimation solution is adapted when the noise is non-stationary. The approach will be tested under a simulation scenario with multiple propagation conditions. Then, this density modeling will be used in Rao-Blackwellised Particle Filter.\",\"PeriodicalId\":341446,\"journal\":{\"name\":\"2010 13th International Conference on Information Fusion\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-07-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 13th International Conference on Information Fusion\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIF.2010.5711829\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 13th International Conference on Information Fusion","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIF.2010.5711829","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

摘要

在卫星导航系统中,经典的定位算法假设观测噪声为白高斯噪声。当信号反射到周围的障碍物上时,这种假设就不正确了。这导致服务的准确性和连续性下降。为了提高定位性能,可以在自适应滤波过程中使用更好的观测噪声密度。本文旨在说明如何利用狄利希-莱混合过程在线跟踪观测密度。这种序列估计方法适用于噪声是非平稳的情况。该方法将在具有多种传播条件的仿真场景下进行测试。然后,这种密度建模将在Rao-Blackwellised粒子过滤器中使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
GNSS pseudorange error density tracking using Dirichlet Process Mixture
In satellite navigation system, classical localization algorithms assume that the observation noise is white-Gaussian. This assumption is not correct when the signal is reflected on the surrounding obstacles. That leads to a decrease of accuracy and of continuity of service. To enhance the localization performances, a better observation noise density can be use in an adapted filtering process. This article aims to show how the Dirich-let Process Mixture can be employed to track the observation density on-line. This sequential estimation solution is adapted when the noise is non-stationary. The approach will be tested under a simulation scenario with multiple propagation conditions. Then, this density modeling will be used in Rao-Blackwellised Particle Filter.
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