• 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

利用低成本 MEMS 惯性测量单元、OBD-II 和数字高度计增强 GNSS，提高城市地区的定位精度。

Augmentation of GNSS by Low-Cost MEMS IMU, OBD-II, and Digital Altimeter for Improved Positioning in Urban Area.

机构信息

School of Electronics and Information Engineering, Korea Aerospace University, Goyang-si 10540, Korea.

出版信息

Sensors (Basel). 2018 Nov 8;18(11):3830. doi: 10.3390/s18113830.

DOI:10.3390/s18113830
PMID:30413086
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6264105/
Abstract

This paper proposes an efficient multi-sensor system to complement GNSS (Global Navigation Satellite System) for improved positioning in urban area. The proposed system augments GNSS by low-cost MEMS IMU (Micro Electro Mechanical Systems Inertial Measurement Unit), OBD (On-Board Diagnostics)-II, and digital altimeter modules. For improved availability of time synchronization in urban area, an adaptive synchronization method is proposed to combine the external PPS (Pulse Per Second) signal and the internal onboard clock. For improved positioning accuracy and availability, a 17-state Kalman filter is formulated for efficient multi-sensor fusion, including OBD-II and digital altimeter modules. A strategy to apply different types of measurement updates is also proposed for improved performance in urban area. Four experiment results with field-collected measurements evaluates the performance of the proposed GNSS/IMU/OBD-II/altimeter system in various aspects, including accuracy, precision, continuity, and availability.

摘要

本文提出了一种高效的多传感器系统，用于补充 GNSS（全球导航卫星系统），以提高城市地区的定位精度。该系统通过低成本的 MEMS 惯性测量单元（IMU）、OBD-II 和数字高度计模块来增强 GNSS。为了提高城市地区时间同步的可用性，提出了一种自适应同步方法，以结合外部 PPS（每秒脉冲）信号和内部板载时钟。为了提高定位精度和可用性，针对高效的多传感器融合，提出了一个 17 状态卡尔曼滤波器，包括 OBD-II 和数字高度计模块。还提出了一种应用不同类型测量更新的策略，以提高城市地区的性能。通过四个使用现场采集测量的实验结果，评估了所提出的 GNSS/IMU/OBD-II/高度计系统在精度、精度、连续性和可用性等各个方面的性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/7a1f6879c05e/sensors-18-03830-g021.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/280aaeb71e5f/sensors-18-03830-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/cabde1459029/sensors-18-03830-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/eac538075a66/sensors-18-03830-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/d6c941eea231/sensors-18-03830-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/2d7151459d29/sensors-18-03830-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/c08c1e088865/sensors-18-03830-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/4e5193e2512b/sensors-18-03830-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/a71c46c27c61/sensors-18-03830-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/32ac16a1853b/sensors-18-03830-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/21e24ac53e92/sensors-18-03830-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/a62cdc438cdd/sensors-18-03830-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/525030612aa4/sensors-18-03830-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/3c33e0203fce/sensors-18-03830-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/1962404c1541/sensors-18-03830-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/d00fe0441997/sensors-18-03830-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/4e636d012e4d/sensors-18-03830-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/cabf53f85063/sensors-18-03830-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/a3fd81fcad6e/sensors-18-03830-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/f973f37d3d70/sensors-18-03830-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/ab6240da0042/sensors-18-03830-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/7a1f6879c05e/sensors-18-03830-g021.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/280aaeb71e5f/sensors-18-03830-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/cabde1459029/sensors-18-03830-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/eac538075a66/sensors-18-03830-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/d6c941eea231/sensors-18-03830-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/2d7151459d29/sensors-18-03830-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/c08c1e088865/sensors-18-03830-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/4e5193e2512b/sensors-18-03830-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/a71c46c27c61/sensors-18-03830-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/32ac16a1853b/sensors-18-03830-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/21e24ac53e92/sensors-18-03830-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/a62cdc438cdd/sensors-18-03830-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/525030612aa4/sensors-18-03830-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/3c33e0203fce/sensors-18-03830-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/1962404c1541/sensors-18-03830-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/d00fe0441997/sensors-18-03830-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/4e636d012e4d/sensors-18-03830-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/cabf53f85063/sensors-18-03830-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/a3fd81fcad6e/sensors-18-03830-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/f973f37d3d70/sensors-18-03830-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/ab6240da0042/sensors-18-03830-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/59ec/6264105/7a1f6879c05e/sensors-18-03830-g021.jpg

相似文献

1
Augmentation of GNSS by Low-Cost MEMS IMU, OBD-II, and Digital Altimeter for Improved Positioning in Urban Area.利用低成本 MEMS 惯性测量单元、OBD-II 和数字高度计增强 GNSS，提高城市地区的定位精度。
Sensors (Basel). 2018 Nov 8;18(11):3830. doi: 10.3390/s18113830.
2
Tightly-Coupled Integration of Multi-GNSS Single-Frequency RTK and MEMS-IMU for Enhanced Positioning Performance.多全球导航卫星系统单频实时动态定位与微机电惯性测量单元的紧密耦合集成以提升定位性能
Sensors (Basel). 2017 Oct 27;17(11):2462. doi: 10.3390/s17112462.
3
A Fuzzy-Innovation-Based Adaptive Kalman Filterfor Enhanced Vehicle Positioning in DenseUrban Environments.基于模糊创新的自适应卡尔曼滤波器，用于增强密集城市环境中的车辆定位。
Sensors (Basel). 2019 Mar 6;19(5):1142. doi: 10.3390/s19051142.
4
Implementation and Performance of a Deeply-Coupled GNSS Receiver with Low-Cost MEMS Inertial Sensors for Vehicle Urban Navigation.一种用于车辆城市导航的集成低成本MEMS惯性传感器的深度耦合GNSS接收机的实现与性能
Sensors (Basel). 2020 Jun 16;20(12):3397. doi: 10.3390/s20123397.
5
Implementation and Analysis of Tightly Coupled Global Navigation Satellite System Precise Point Positioning/Inertial Navigation System (GNSS PPP/INS) with Insufficient Satellites for Land Vehicle Navigation.紧耦合全球导航卫星系统精密单点定位/惯性导航系统（GNSS PPP/INS）在陆地车辆导航中卫星不足的实现与分析。
Sensors (Basel). 2018 Dec 6;18(12):4305. doi: 10.3390/s18124305.
6
A Multi-Sensor Tight Fusion Method Designed for Vehicle Navigation.一种专为车辆导航设计的多传感器紧密融合方法。
Sensors (Basel). 2020 Apr 30;20(9):2551. doi: 10.3390/s20092551.
7
MEMS IMU Error Mitigation Using Rotation Modulation Technique.基于旋转调制技术的微机电系统惯性测量单元误差抑制
Sensors (Basel). 2016 Nov 29;16(12):2017. doi: 10.3390/s16122017.
8
Performance Characterization of GNSS/IMU/DVL Integration under Real Maritime Jamming Conditions.GNSS/IMU/DVL 集成在真实海况干扰下的性能特征分析。
Sensors (Basel). 2018 Sep 5;18(9):2954. doi: 10.3390/s18092954.
9
Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; A GNSS/IMU Case Study.深度卡尔曼滤波器：同步多传感器集成与建模；一个全球导航卫星系统/惯性测量单元的案例研究。
Sensors (Basel). 2018 Apr 24;18(5):1316. doi: 10.3390/s18051316.
10
Benefits of Multi-Constellation/Multi-Frequency GNSS in a Tightly Coupled GNSS/IMU/Odometry Integration Algorithm.多星座/多频率 GNSS 在紧耦合 GNSS/IMU/里程计组合算法中的优势。
Sensors (Basel). 2018 Sep 12;18(9):3052. doi: 10.3390/s18093052.

引用本文的文献

1
Error Characteristic Analysis and Filtering Algorithm for GNSS Time-Series Data.GNSS时间序列数据的误差特征分析与滤波算法
Sensors (Basel). 2025 Jan 9;25(2):361. doi: 10.3390/s25020361.
2
Twist-n-Sync: Software Clock Synchronization with Microseconds Accuracy Using MEMS-Gyroscopes.Twist-n-Sync：使用MEMS陀螺仪实现微秒级精度的软件时钟同步
Sensors (Basel). 2020 Dec 24;21(1):68. doi: 10.3390/s21010068.

本文引用的文献

1
INS/GPS/LiDAR Integrated Navigation System for Urban and Indoor Environments Using Hybrid Scan Matching Algorithm.基于混合扫描匹配算法的用于城市和室内环境的 INS/GPS/激光雷达集成导航系统
Sensors (Basel). 2015 Sep 15;15(9):23286-302. doi: 10.3390/s150923286.
2
Benefits of combined GPS/GLONASS with low-cost MEMS IMUs for vehicular urban navigation.组合 GPS/GLONASS 和低成本 MEMS IMU 对车载城市导航的优势。
Sensors (Basel). 2012;12(4):5134-58. doi: 10.3390/s120405134. Epub 2012 Apr 19.
3
FPGA-based real-time embedded system for RISS/GPS integrated navigation.
基于 FPGA 的 RISS/GPS 组合导航实时嵌入式系统。
Sensors (Basel). 2012;12(1):115-47. doi: 10.3390/s120100115. Epub 2011 Dec 22.