• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

一种用于INS/DVL组合导航系统的IMM辅助ZUPT方法。

An IMM-Aided ZUPT Methodology for an INS/DVL Integrated Navigation System.

作者信息

Yao Yiqing, Xu Xiaosu, Xu Xiang

机构信息

Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.

出版信息

Sensors (Basel). 2017 Sep 5;17(9):2030. doi: 10.3390/s17092030.

DOI:10.3390/s17092030
PMID:28872602
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5621470/
Abstract

Inertial navigation system (INS)/Doppler velocity log (DVL) integration is the most common navigation solution for underwater vehicles. Due to the complex underwater environment, the velocity information provided by DVL always contains some errors. To improve navigation accuracy, zero velocity update (ZUPT) technology is considered, which is an effective algorithm for land vehicles to mitigate the navigation error during the pure INS mode. However, in contrast to ground vehicles, the ZUPT solution cannot be used directly for underwater vehicles because of the existence of the water current. In order to leverage the strengths of the ZUPT method and the INS/DVL solution, an interactive multiple model (IMM)-aided ZUPT methodology for the INS/DVL-integrated underwater navigation system is proposed. Both the INS/DVL and INS/ZUPT models are constructed and operated in parallel, with weights calculated according to their innovations and innovation covariance matrices. Simulations are conducted to evaluate the proposed algorithm. The results indicate that the IMM-aided ZUPT solution outperforms both the INS/DVL solution and the INS/ZUPT solution in the underwater environment, which can properly distinguish between the ZUPT and non-ZUPT conditions. In addition, during DVL outage, the effectiveness of the proposed algorithm is also verified.

摘要

惯性导航系统(INS)/多普勒速度计(DVL)组合是水下航行器最常用的导航解决方案。由于水下环境复杂,DVL提供的速度信息总是包含一些误差。为提高导航精度,考虑采用零速更新(ZUPT)技术,这是陆地车辆在纯INS模式下减轻导航误差的一种有效算法。然而,与地面车辆不同,由于水流的存在,ZUPT解决方案不能直接用于水下航行器。为了利用ZUPT方法和INS/DVL解决方案的优势,提出了一种用于INS/DVL组合水下导航系统的交互式多模型(IMM)辅助ZUPT方法。构建并并行运行INS/DVL和INS/ZUPT模型,根据它们的残差和残差协方差矩阵计算权重。进行了仿真以评估所提算法。结果表明,IMM辅助ZUPT解决方案在水下环境中优于INS/DVL解决方案和INS/ZUPT解决方案,能够正确区分ZUPT和非ZUPT情况。此外,在DVL失效期间,所提算法的有效性也得到了验证。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/addc477e3a20/sensors-17-02030-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/bf4668a271b4/sensors-17-02030-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/50e637c973e7/sensors-17-02030-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/48ed5ca0940a/sensors-17-02030-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/a9799f7274ad/sensors-17-02030-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/abaf6dd2dafe/sensors-17-02030-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/61d906fa4133/sensors-17-02030-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/d4634379f871/sensors-17-02030-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/c2e5ba5f6891/sensors-17-02030-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/f49bdccec098/sensors-17-02030-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/c6ae09d5982f/sensors-17-02030-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/1568b8590af4/sensors-17-02030-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/55859b11dc86/sensors-17-02030-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/69ab9ab8bfa2/sensors-17-02030-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/1d9683e266c1/sensors-17-02030-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/651fb3c6563e/sensors-17-02030-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/1c70fdc56e9f/sensors-17-02030-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/fe19054a2ed2/sensors-17-02030-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/0dc40b809a52/sensors-17-02030-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/addc477e3a20/sensors-17-02030-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/bf4668a271b4/sensors-17-02030-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/50e637c973e7/sensors-17-02030-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/48ed5ca0940a/sensors-17-02030-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/a9799f7274ad/sensors-17-02030-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/abaf6dd2dafe/sensors-17-02030-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/61d906fa4133/sensors-17-02030-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/d4634379f871/sensors-17-02030-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/c2e5ba5f6891/sensors-17-02030-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/f49bdccec098/sensors-17-02030-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/c6ae09d5982f/sensors-17-02030-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/1568b8590af4/sensors-17-02030-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/55859b11dc86/sensors-17-02030-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/69ab9ab8bfa2/sensors-17-02030-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/1d9683e266c1/sensors-17-02030-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/651fb3c6563e/sensors-17-02030-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/1c70fdc56e9f/sensors-17-02030-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/fe19054a2ed2/sensors-17-02030-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/0dc40b809a52/sensors-17-02030-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e8e/5621470/addc477e3a20/sensors-17-02030-g019.jpg

相似文献

1
An IMM-Aided ZUPT Methodology for an INS/DVL Integrated Navigation System.一种用于INS/DVL组合导航系统的IMM辅助ZUPT方法。
Sensors (Basel). 2017 Sep 5;17(9):2030. doi: 10.3390/s17092030.
2
A Tightly Integrated Navigation Method of SINS, DVL, and PS Based on RIMM in the Complex Underwater Environment.基于 RIMM 的 SINS、DVL 和 PS 紧组合导航方法在复杂水下环境中的应用。
Sensors (Basel). 2022 Dec 4;22(23):9479. doi: 10.3390/s22239479.
3
Performance Enhancement of a USV INS/CNS/DVL Integration Navigation System Based on an Adaptive Information Sharing Factor Federated Filter.基于自适应信息共享因子联邦滤波器的无人水面舰艇惯性导航系统/组合导航系统/多普勒测速仪组合导航系统性能增强
Sensors (Basel). 2017 Feb 3;17(2):239. doi: 10.3390/s17020239.
4
Inertial Navigation System/Doppler Velocity Log (INS/DVL) Fusion with Partial DVL Measurements.基于部分多普勒测速仪测量值的惯性导航系统/多普勒测速仪(INS/DVL)融合
Sensors (Basel). 2017 Feb 22;17(2):415. doi: 10.3390/s17020415.
5
Adaptive Federated IMM Filter for AUV Integrated Navigation Systems.用于自主水下航行器集成导航系统的自适应联邦交互式多模型滤波器
Sensors (Basel). 2020 Nov 28;20(23):6806. doi: 10.3390/s20236806.
6
A virtual velocity-based integrated navigation method for strapdown inertial navigation system and Doppler velocity log coupled with unknown current.一种基于虚拟速度的捷联惯性导航系统与多普勒测速仪耦合未知海流的组合导航方法。
Rev Sci Instrum. 2022 Jun 1;93(6):065112. doi: 10.1063/5.0089117.
7
A novel INS and Doppler sensors calibration method for long range underwater vehicle navigation.一种新型 INS 和多普勒传感器校准方法,用于远程水下航行器导航。
Sensors (Basel). 2013 Oct 28;13(11):14583-600. doi: 10.3390/s131114583.
8
Research on Error Correction Technology in Underwater SINS/DVL Integrated Positioning and Navigation.水下 SINS/DVL 组合定位导航误差修正技术研究
Sensors (Basel). 2023 May 12;23(10):4700. doi: 10.3390/s23104700.
9
A Robust INS/USBL/DVL Integrated Navigation Algorithm Using Graph Optimization.基于图优化的稳健 INS/USBL/DVL 组合导航算法。
Sensors (Basel). 2023 Jan 12;23(2):916. doi: 10.3390/s23020916.
10
In-motion coarse alignment method for SINS/DVL with the attitude dynamics.基于姿态动力学的捷联惯导系统/多普勒测速仪运动中粗对准方法
ISA Trans. 2020 Oct;105:377-386. doi: 10.1016/j.isatra.2020.05.033. Epub 2020 May 27.

引用本文的文献

1
Muometric positioning system (muPS) utilizing direction vectors of cosmic-ray muons for wireless indoor navigation at a centimeter-level accuracy.利用宇宙射线μ子的方向向量实现厘米级精度无线室内导航的μ测量定位系统(muPS)。
Sci Rep. 2023 Sep 15;13(1):15272. doi: 10.1038/s41598-023-41910-y.
2
A Robust INS/USBL/DVL Integrated Navigation Algorithm Using Graph Optimization.基于图优化的稳健 INS/USBL/DVL 组合导航算法。
Sensors (Basel). 2023 Jan 12;23(2):916. doi: 10.3390/s23020916.
3
A Tightly Integrated Navigation Method of SINS, DVL, and PS Based on RIMM in the Complex Underwater Environment.

本文引用的文献

1
Drift Reduction in Pedestrian Navigation System by Exploiting Motion Constraints and Magnetic Field.利用运动约束和磁场减少行人导航系统中的漂移
Sensors (Basel). 2016 Sep 9;16(9):1455. doi: 10.3390/s16091455.
基于 RIMM 的 SINS、DVL 和 PS 紧组合导航方法在复杂水下环境中的应用。
Sensors (Basel). 2022 Dec 4;22(23):9479. doi: 10.3390/s22239479.
4
Application of Initial Bias Estimation Method for Inertial Navigation System (INS)/Doppler Velocity Log (DVL) and INS/DVL/Gyrocompass Using Micro-Electro-Mechanical System Sensors.微机电系统传感器在惯性导航系统(INS)/多普勒速度计(DVL)和 INS/DVL/陀螺罗经初始偏差估计方法中的应用。
Sensors (Basel). 2022 Jul 17;22(14):5334. doi: 10.3390/s22145334.
5
Machine Learning Improvements to Human Motion Tracking with IMUs.基于惯性测量单元的人体运动追踪的机器学习改进。
Sensors (Basel). 2020 Nov 9;20(21):6383. doi: 10.3390/s20216383.
6
A Fault-Tolerant Polar Grid SINS/DVL/USBL Integrated Navigation Algorithm Based on the Centralized Filter and Relative Position Measurement.基于集中滤波器和相对位置测量的容错极区网格惯导/多普勒计程仪/超短基线组合导航算法
Sensors (Basel). 2019 Sep 10;19(18):3899. doi: 10.3390/s19183899.
7
Square-Root Unscented Information Filter and Its Application in SINS/DVL Integrated Navigation.平方根无迹信息滤波及其在 SINS/DVL 组合导航中的应用。
Sensors (Basel). 2018 Jun 28;18(7):2069. doi: 10.3390/s18072069.
8
Enhanced Pedestrian Navigation Based on Course Angle Error Estimation Using Cascaded Kalman Filters.基于级联卡尔曼滤波器的航向角误差估计的增强型行人导航
Sensors (Basel). 2018 Apr 21;18(4):1281. doi: 10.3390/s18041281.
9
A Novel Grid SINS/DVL Integrated Navigation Algorithm for Marine Application.一种用于海洋应用的新型网格捷联惯导/多普勒测速仪组合导航算法
Sensors (Basel). 2018 Jan 26;18(2):364. doi: 10.3390/s18020364.