GPS拒止区无人机自适应CKF协同导航方法

Yingrong Yu, Siting Peng, Qingdong Li, Xiwang Dong, Z. Ren
{"title":"GPS拒止区无人机自适应CKF协同导航方法","authors":"Yingrong Yu, Siting Peng, Qingdong Li, Xiwang Dong, Z. Ren","doi":"10.1109/GNCC42960.2018.9018972","DOIUrl":null,"url":null,"abstract":"Unmanned Aerial Vehicles (UAVs), when used in a formation setting, can be more advantageous. The concerted operation of UAV formations has many potential applications, such as cooperative reconnaissance, formation combat and search and rescue in mountainous regions. Nevertheless, modern navigation systems for UAVs are not able to guarantee the precision of pose estimation when GPS is unavailable in complex environment. The IMU drifts of navigation systems can cause poor calculation accuracy of position, velocity and attitude of all UAVs in the group within dozens of seconds. To solve this problem, this paper puts forward a new cooperative navigation method based on adaptive Cubature Kalman Filter which shares the relative observations between the UAVs and fuses these data with direct measurements from IMU to obtain better navigation performance. The simulation results demonstrate the validity of the proposed method.","PeriodicalId":6623,"journal":{"name":"2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC)","volume":"49 1","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Cooperative Navigation Method Based on Adaptive CKF for UAVs In GPS Denied Areas\",\"authors\":\"Yingrong Yu, Siting Peng, Qingdong Li, Xiwang Dong, Z. Ren\",\"doi\":\"10.1109/GNCC42960.2018.9018972\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Unmanned Aerial Vehicles (UAVs), when used in a formation setting, can be more advantageous. The concerted operation of UAV formations has many potential applications, such as cooperative reconnaissance, formation combat and search and rescue in mountainous regions. Nevertheless, modern navigation systems for UAVs are not able to guarantee the precision of pose estimation when GPS is unavailable in complex environment. The IMU drifts of navigation systems can cause poor calculation accuracy of position, velocity and attitude of all UAVs in the group within dozens of seconds. To solve this problem, this paper puts forward a new cooperative navigation method based on adaptive Cubature Kalman Filter which shares the relative observations between the UAVs and fuses these data with direct measurements from IMU to obtain better navigation performance. The simulation results demonstrate the validity of the proposed method.\",\"PeriodicalId\":6623,\"journal\":{\"name\":\"2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC)\",\"volume\":\"49 1\",\"pages\":\"1-6\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GNCC42960.2018.9018972\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GNCC42960.2018.9018972","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

无人驾驶飞行器(uav),当用于编队设置时,可以更有利。无人机编队协同作战具有协同侦察、编队作战、山区搜救等多种潜在应用。然而,现代无人机导航系统在复杂环境下没有GPS时,无法保证姿态估计的精度。导航系统的IMU漂移会导致群内所有无人机在几十秒内的位置、速度和姿态计算精度较差。针对这一问题,本文提出了一种基于自适应Cubature Kalman滤波的协同导航方法,该方法通过共享无人机之间的相关观测数据,并将这些数据与IMU的直接测量数据融合,以获得更好的导航性能。仿真结果验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cooperative Navigation Method Based on Adaptive CKF for UAVs In GPS Denied Areas
Unmanned Aerial Vehicles (UAVs), when used in a formation setting, can be more advantageous. The concerted operation of UAV formations has many potential applications, such as cooperative reconnaissance, formation combat and search and rescue in mountainous regions. Nevertheless, modern navigation systems for UAVs are not able to guarantee the precision of pose estimation when GPS is unavailable in complex environment. The IMU drifts of navigation systems can cause poor calculation accuracy of position, velocity and attitude of all UAVs in the group within dozens of seconds. To solve this problem, this paper puts forward a new cooperative navigation method based on adaptive Cubature Kalman Filter which shares the relative observations between the UAVs and fuses these data with direct measurements from IMU to obtain better navigation performance. The simulation results demonstrate the validity of the proposed method.
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
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信