• 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

一种用于提高无人机在高海拔遮蔽区域导航性能的在线 SBAS 服务。

An Online SBAS Service to Improve Drone Navigation Performance in High-Elevation Masked Areas.

作者信息

Yoon Hyojung, Seok Hyojeong, Lim Cheolsoon, Park Byungwoon

机构信息

School of Aerospace Engineering, Sejong University, Seoul 05006, Korea.

Security/ANS Certification Department, Aviation Certification Division, Korea Institute of Aviation Safety Technology, Incheon 22581, Korea.

出版信息

Sensors (Basel). 2020 May 27;20(11):3047. doi: 10.3390/s20113047.

DOI:10.3390/s20113047
PMID:32471237
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7309144/
Abstract

Owing to the high demand for drone operation in high-elevation masked areas, it is necessary to develop a more effective method of transmitting and applying Satellite-Based Augmentation System (SBAS) messages for drones. This study proposes an onboard module including correction conversion, integrity information calculation, and fast initialization requests, which can enable the application of an online SBAS to drone operation. The proposed system not only improves the position accuracy with timely and proper protection levels in an open sky, but also reduces the initialization time from 70-100 s to 1 s, enabling a drone of short endurance to perform its mission successfully. In SBAS signal-denied cases, the position accuracy was improved by 40% and the uncorrected 13.4 m vertical error was reduced to 5.6 m by applying an SBAS message delivered online. The protection levels calculated with the accurate position regardless of the current location could denote the thrust level and availability of the navigation solution. The proposed system can practically solve the drawbacks of the current SBAS, considering the characteristics of the low-cost receivers on the market. Our proposed system is expected to be a useful and practical solution to integrate drones into the airspace in the near future.

摘要

由于在高海拔遮蔽区域对无人机操作有很高需求，有必要开发一种更有效的方法来为无人机传输和应用基于卫星的增强系统（SBAS）消息。本研究提出了一种机载模块，包括校正转换、完整性信息计算和快速初始化请求，这可以使在线SBAS应用于无人机操作。所提出的系统不仅在开阔天空中以及时且适当的保护级别提高了定位精度，还将初始化时间从70 - 100秒减少到1秒，使续航时间短的无人机能够成功执行任务。在SBAS信号被拒的情况下，通过应用在线传输的SBAS消息，定位精度提高了40%，未校正的13.4米垂直误差降低到了5.6米。无论当前位置如何，利用精确位置计算出的保护级别都可以表示导航解的推力水平和可用性。考虑到市场上低成本接收机的特性，所提出的系统实际上可以解决当前SBAS的缺点。预计我们所提出的系统在不久的将来将成为将无人机集成到空域的一种有用且实用的解决方案。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/cbea8c0fbe6d/sensors-20-03047-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/170de7feb824/sensors-20-03047-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/049b77a3a1f1/sensors-20-03047-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/d97bda719b75/sensors-20-03047-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/d34e3b8b05d2/sensors-20-03047-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/dceee83b4b49/sensors-20-03047-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/9dab61d0e579/sensors-20-03047-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/bbc0ee2831c3/sensors-20-03047-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/37d6364f023c/sensors-20-03047-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/5de66974fd77/sensors-20-03047-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/c3ec8f83c012/sensors-20-03047-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/a56f54efe425/sensors-20-03047-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/1e37624f7421/sensors-20-03047-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/52d44f75d22f/sensors-20-03047-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/c0ff6f8c7c22/sensors-20-03047-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/4948ce3bebb3/sensors-20-03047-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/16c5c03a62ad/sensors-20-03047-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/a3032cae8765/sensors-20-03047-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/cbea8c0fbe6d/sensors-20-03047-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/170de7feb824/sensors-20-03047-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/049b77a3a1f1/sensors-20-03047-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/d97bda719b75/sensors-20-03047-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/d34e3b8b05d2/sensors-20-03047-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/dceee83b4b49/sensors-20-03047-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/9dab61d0e579/sensors-20-03047-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/bbc0ee2831c3/sensors-20-03047-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/37d6364f023c/sensors-20-03047-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/5de66974fd77/sensors-20-03047-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/c3ec8f83c012/sensors-20-03047-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/a56f54efe425/sensors-20-03047-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/1e37624f7421/sensors-20-03047-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/52d44f75d22f/sensors-20-03047-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/c0ff6f8c7c22/sensors-20-03047-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/4948ce3bebb3/sensors-20-03047-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/16c5c03a62ad/sensors-20-03047-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/a3032cae8765/sensors-20-03047-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1eb8/7309144/cbea8c0fbe6d/sensors-20-03047-g018.jpg

相似文献

1
An Online SBAS Service to Improve Drone Navigation Performance in High-Elevation Masked Areas.一种用于提高无人机在高海拔遮蔽区域导航性能的在线 SBAS 服务。
Sensors (Basel). 2020 May 27;20(11):3047. doi: 10.3390/s20113047.
2
Satellite Navigation Message Authentication in GNSS: Research on Message Scheduler for SBAS L1.全球导航卫星系统中的卫星导航消息认证：星基增强系统L1消息调度器研究
Sensors (Basel). 2024 Jan 7;24(2):0. doi: 10.3390/s24020360.
3
Monitoring Aircraft Position Using EGNOS Data for the SBAS APV Approach to the Landing Procedure.利用 EGNOS 数据监测飞机位置，为 SBAS APV 进近着陆程序提供支持。
Sensors (Basel). 2020 Mar 30;20(7):1945. doi: 10.3390/s20071945.
4
Optimal Divergence-Free Hatch Filter for GNSS Single-Frequency Measurement.用于全球导航卫星系统单频测量的最优无散度哈奇滤波器
Sensors (Basel). 2017 Feb 24;17(3):448. doi: 10.3390/s17030448.
5
Efficient Methods of Utilizing Multi-SBAS Corrections in Multi-GNSS Positioning.多 GNSS 定位中多 SBAS 校正的有效利用方法。
Sensors (Basel). 2020 Jan 1;20(1):256. doi: 10.3390/s20010256.
6
Fault-Free Protection Level Equation for CLAS PPP-RTK and Experimental Evaluations.CLAS PPP-RTK的无故障保护级别方程及实验评估
Sensors (Basel). 2022 May 7;22(9):3570. doi: 10.3390/s22093570.
7
Real-Time Single Frequency Precise Point Positioning Using SBAS Corrections.使用星基增强系统（SBAS）校正的实时单频精密单点定位
Sensors (Basel). 2016 Aug 10;16(8):1261. doi: 10.3390/s16081261.
8
Integrity Concept for Maritime Autonomous Surface Ships' Position Sensors.海上自主水面船舶位置传感器的完整性概念
Sensors (Basel). 2020 Apr 7;20(7):2075. doi: 10.3390/s20072075.
9
The EGNOS Augmentation in Maritime Navigation.EGNOS 增强在航海中的应用。
Sensors (Basel). 2022 Jan 20;22(3):775. doi: 10.3390/s22030775.
10
Drones and global navigation satellite systems: current evidence from polar scientists.无人机与全球导航卫星系统：极地科学家的当前证据
R Soc Open Sci. 2020 Mar 18;7(3):191494. doi: 10.1098/rsos.191494. eCollection 2020 Mar.

引用本文的文献

1
Drone-assisted time-varying magnetic field analysis for fault diagnosis in grounding grids.用于接地网故障诊断的无人机辅助时变磁场分析
PLoS One. 2025 Jun 17;20(6):e0325845. doi: 10.1371/journal.pone.0325845. eCollection 2025.
2
Analysis of GPS/EGNOS Positioning Quality Using Different Ionospheric Models in UAV Navigation.利用不同电离层模型在无人机导航中分析 GPS/EGNOS 定位质量。
Sensors (Basel). 2023 Jan 18;23(3):1112. doi: 10.3390/s23031112.
3
Inherent Limitations of Smartphone GNSS Positioning and Effective Methods to Increase the Accuracy Utilizing Dual-Frequency Measurements.

本文引用的文献

1
Drones and global navigation satellite systems: current evidence from polar scientists.无人机与全球导航卫星系统：极地科学家的当前证据
R Soc Open Sci. 2020 Mar 18;7(3):191494. doi: 10.1098/rsos.191494. eCollection 2020 Mar.
2
GNSS Performance Modelling and Augmentation for Urban Air Mobility.城市空中交通的 GNSS 性能建模与增强。
Sensors (Basel). 2019 Sep 27;19(19):4209. doi: 10.3390/s19194209.
3
Precise Point Positioning Using World's First Dual-Frequency GPS/GALILEO Smartphone.使用全球首款双频GPS/伽利略系统智能手机进行精确点位定位。
智能手机 GNSS 定位的固有局限性及利用双频测量提高精度的有效方法。
Sensors (Basel). 2022 Dec 15;22(24):9879. doi: 10.3390/s22249879.
4
Communication Interface Manager for Improving Performance of Heterogeneous UAV Networks.用于提高异构无人机网络性能的通信接口管理器
Sensors (Basel). 2021 Jun 22;21(13):4255. doi: 10.3390/s21134255.
5
A Low-Cost, High-Precision Vehicle Navigation System for Deep Urban Multipath Environment Using TDCP Measurements.一种利用TDCP测量的适用于深度城市多径环境的低成本、高精度车辆导航系统。
Sensors (Basel). 2020 Jun 7;20(11):3254. doi: 10.3390/s20113254.
Sensors (Basel). 2019 Jun 6;19(11):2593. doi: 10.3390/s19112593.
4
Low-Cost Curb Detection and Localization System Using Multiple Ultrasonic Sensors.基于多个超声波传感器的低成本路缘检测与定位系统。
Sensors (Basel). 2019 Mar 21;19(6):1389. doi: 10.3390/s19061389.
5
Position Accuracy Improvement by Implementing the DGNSS-CP Algorithm in Smartphones.通过在智能手机中实施差分全球导航卫星系统-载波相位（DGNSS-CP）算法提高定位精度
Sensors (Basel). 2016 Jun 18;16(6):910. doi: 10.3390/s16060910.
6
A comprehensive method for GNSS data quality determination to improve ionospheric data analysis.一种用于确定全球导航卫星系统（GNSS）数据质量以改进电离层数据分析的综合方法。
Sensors (Basel). 2014 Aug 14;14(8):14971-93. doi: 10.3390/s140814971.