Vehicle Dynamic Model Based Navigation Algorithm Requirement Analysis for Improving High Precision Inertial Navigation System

Bo-Sung Ko, Yong-Hun Kim, Joo-Han Lee, So-Jin Park, Seung Hyo Park, Jin Woo Song
{"title":"Vehicle Dynamic Model Based Navigation Algorithm Requirement Analysis for Improving High Precision Inertial Navigation System","authors":"Bo-Sung Ko, Yong-Hun Kim, Joo-Han Lee, So-Jin Park, Seung Hyo Park, Jin Woo Song","doi":"10.5302/j.icros.2023.23.0112","DOIUrl":null,"url":null,"abstract":"We propose an idea of using VDM (Vehicle Dynamic Model) as a constraint of INS (Inertial Navigation System) for improving the long term navigation performance. In order to suppress long term INS error, a constrained Kalman filter is applied. Before developing the high precision INS with various sensors and techniques, M&S (Modeling & Simulation) step is performed to define and validate the necessary requirements of the development, which here in this research, the requirement of VDM quality is analyzed. M&S is constructed using a 6-DOF submarine dynamic model and a guidance-navigation control model. It is assumed that the navigation sensor is consisted with an inertial measurement unit, EM-Log (Electromagnetic Log), and DM (Depth Module). Monte-Carlo simulation results confirm the effectiveness of the VDM employed by the constrained Kalman filter and requirements for VDM error are also analyzed.","PeriodicalId":38644,"journal":{"name":"Journal of Institute of Control, Robotics and Systems","volume":"35 5","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Institute of Control, Robotics and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5302/j.icros.2023.23.0112","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Mathematics","Score":null,"Total":0}
引用次数: 0

Abstract

We propose an idea of using VDM (Vehicle Dynamic Model) as a constraint of INS (Inertial Navigation System) for improving the long term navigation performance. In order to suppress long term INS error, a constrained Kalman filter is applied. Before developing the high precision INS with various sensors and techniques, M&S (Modeling & Simulation) step is performed to define and validate the necessary requirements of the development, which here in this research, the requirement of VDM quality is analyzed. M&S is constructed using a 6-DOF submarine dynamic model and a guidance-navigation control model. It is assumed that the navigation sensor is consisted with an inertial measurement unit, EM-Log (Electromagnetic Log), and DM (Depth Module). Monte-Carlo simulation results confirm the effectiveness of the VDM employed by the constrained Kalman filter and requirements for VDM error are also analyzed.
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
基于车辆动态模型的导航算法改进高精度惯性导航系统需求分析
为了提高惯性导航系统的长期导航性能,提出了一种利用车辆动态模型(Vehicle Dynamic Model, VDM)约束惯性导航系统的思想。为了抑制惯导系统的长期误差,采用了约束卡尔曼滤波器。在开发采用各种传感器和技术的高精度惯导系统之前,首先进行建模与仿真(M&S)步骤,以定义和验证开发的必要要求,在本研究中,分析了对VDM质量的要求。采用六自由度潜艇动力学模型和制导导航控制模型构建了M&S系统。假设导航传感器由惯性测量单元、EM-Log(电磁日志)和DM(深度模块)组成。蒙特卡罗仿真结果证实了约束卡尔曼滤波器所采用的VDM的有效性,并分析了对VDM误差的要求。
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
CiteScore
1.50
自引率
0.00%
发文量
128
期刊最新文献
Proposal of MRFScore and a Regression Model for Identification of Music Relationship Indicator Mixed Reality-based Structure Placement Verification System Using AR Marker Optimal Parameter Estimation for Topological Descriptor Based Sonar Image Matching in Autonomous Underwater Robots 3D Space Object and Road Detection for Autonomous Vehicles Using Monocular Camera Images and Deep Learning Algorithms Optimization Methods for Non-linear Least Squares
×
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