轨道跟踪问题的统一方法

J. Kent, Shambo Bhattacharjee, W. Faber, I. Hussein
{"title":"轨道跟踪问题的统一方法","authors":"J. Kent, Shambo Bhattacharjee, W. Faber, I. Hussein","doi":"10.1109/MFI49285.2020.9235258","DOIUrl":null,"url":null,"abstract":"Consider an object in orbit about the earth for which a sequence of angles-only measurements is made. This paper looks in detail at a one-step update for the filtering problem. Although the problem appears very nonlinear at first sight, it can be almost reduced to the standard linear Kalman filter by a careful formulation. The key features of this formulation are (1) the use of a local or adapted basis rather than a fixed basis for three-dimensional Euclidean space and the use of structural rather than ambient coordinates to represent the state, (2) the development of a novel \"normal:conditional- normal\" distribution to described the propagated position of the state, and (3) the development of a novel \"Observation- Centered\" Kalman filter to update the state distribution.A major advantage of this unified approach is that it gives a closed form filter which is highly accurate under a wide range of conditions, including high initial uncertainty, high eccentricity and long propagation times.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A Unified Approach to The Orbital Tracking Problem\",\"authors\":\"J. Kent, Shambo Bhattacharjee, W. Faber, I. Hussein\",\"doi\":\"10.1109/MFI49285.2020.9235258\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Consider an object in orbit about the earth for which a sequence of angles-only measurements is made. This paper looks in detail at a one-step update for the filtering problem. Although the problem appears very nonlinear at first sight, it can be almost reduced to the standard linear Kalman filter by a careful formulation. The key features of this formulation are (1) the use of a local or adapted basis rather than a fixed basis for three-dimensional Euclidean space and the use of structural rather than ambient coordinates to represent the state, (2) the development of a novel \\\"normal:conditional- normal\\\" distribution to described the propagated position of the state, and (3) the development of a novel \\\"Observation- Centered\\\" Kalman filter to update the state distribution.A major advantage of this unified approach is that it gives a closed form filter which is highly accurate under a wide range of conditions, including high initial uncertainty, high eccentricity and long propagation times.\",\"PeriodicalId\":446154,\"journal\":{\"name\":\"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MFI49285.2020.9235258\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MFI49285.2020.9235258","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

考虑地球轨道上的一个物体,对它进行了一系列只测量角度的测量。本文详细介绍了过滤问题的一步更新。虽然这个问题乍一看非常非线性,但通过仔细的表述,它几乎可以简化为标准的线性卡尔曼滤波。该公式的主要特点是:(1)使用局部或适应基而不是三维欧几里得空间的固定基,并使用结构坐标而不是环境坐标来表示状态,(2)发展了一种新的“正态:条件正态”分布来描述状态的传播位置,以及(3)发展了一种新的“以观测为中心”的卡尔曼滤波器来更新状态分布。这种统一方法的一个主要优点是,它提供了一个封闭形式的滤波器,在广泛的条件下,包括高初始不确定性,高偏心和长传播时间,都是高精度的。
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
A Unified Approach to The Orbital Tracking Problem
Consider an object in orbit about the earth for which a sequence of angles-only measurements is made. This paper looks in detail at a one-step update for the filtering problem. Although the problem appears very nonlinear at first sight, it can be almost reduced to the standard linear Kalman filter by a careful formulation. The key features of this formulation are (1) the use of a local or adapted basis rather than a fixed basis for three-dimensional Euclidean space and the use of structural rather than ambient coordinates to represent the state, (2) the development of a novel "normal:conditional- normal" distribution to described the propagated position of the state, and (3) the development of a novel "Observation- Centered" Kalman filter to update the state distribution.A major advantage of this unified approach is that it gives a closed form filter which is highly accurate under a wide range of conditions, including high initial uncertainty, high eccentricity and long propagation times.
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
OAFuser: Online Adaptive Extended Object Tracking and Fusion using automotive Radar Detections Observability driven Multi-modal Line-scan Camera Calibration Localization and velocity estimation based on multiple bistatic measurements A Continuous Probabilistic Origin Association Filter for Extended Object Tracking Towards Automatic Classification of Fragmented Rock Piles via Proprioceptive Sensing and Wavelet Analysis
×
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