RISS/GPS紧密耦合集成的非线性滤波

J. Georgy, A. Noureldin, Z. Syed, C. Goodall
{"title":"RISS/GPS紧密耦合集成的非线性滤波","authors":"J. Georgy, A. Noureldin, Z. Syed, C. Goodall","doi":"10.1109/PLANS.2010.5507327","DOIUrl":null,"url":null,"abstract":"The integration of Global Positioning System (GPS), inertial sensors and other motion sensors inside land vehicles enable reliable positioning in challenging GPS environments. GPS signals may suffer from blockage in urban canyons and tunnels resulting in interrupted positioning information. Inertial sensors are standalone sensors that can be integrated with GPS and can bridge the blockage periods as they do not rely on any external signals. Recently, miniaturized Micro-Electro-Mechanical Systems (MEMS)-based inertial sensors are abundantly used for vehicle safety applications such as air-bag deployment, roll-over detection, etc. These sensors can be used as inertial navigation system (INS) after integrating with GPS for reliable navigation solution even in denied GPS signal environments. The traditional technique for this integration is based on Kalman filter (KF) with a dedicated inertial sensor module consisting of three orthogonal gyros and three orthogonal accelerometers. This research targets a low cost navigation solution for land vehicles and hence it utilizes a reduced inertial sensor system (RISS) consisting of MEMS-based single axis gyro and a dual axis accelerometer. Additionally, the vehicle's odometer is used and an integrated 3D navigation solution is achieved. To improve the positioning accuracy a nonlinear filtering technique, particle filter (PF) is used to avoid linearization errors. Because of PF ability to deal directly with nonlinear models, it can accommodate arbitrary sensor characteristics and motion dynamics. Consequently, tightly coupled integration which has a nonlinear measurement model can be directly used in PF without introducing any errors. An enhanced version of PF is implemented known as Mixture PF and the performance of this method is examined by actual road tests in a land vehicle and compared with KF.","PeriodicalId":94036,"journal":{"name":"IEEE/ION Position Location and Navigation Symposium : [proceedings]. IEEE/ION Position Location and Navigation Symposium","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"Nonlinear filtering for tightly coupled RISS/GPS integration\",\"authors\":\"J. Georgy, A. Noureldin, Z. Syed, C. Goodall\",\"doi\":\"10.1109/PLANS.2010.5507327\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The integration of Global Positioning System (GPS), inertial sensors and other motion sensors inside land vehicles enable reliable positioning in challenging GPS environments. GPS signals may suffer from blockage in urban canyons and tunnels resulting in interrupted positioning information. Inertial sensors are standalone sensors that can be integrated with GPS and can bridge the blockage periods as they do not rely on any external signals. Recently, miniaturized Micro-Electro-Mechanical Systems (MEMS)-based inertial sensors are abundantly used for vehicle safety applications such as air-bag deployment, roll-over detection, etc. These sensors can be used as inertial navigation system (INS) after integrating with GPS for reliable navigation solution even in denied GPS signal environments. The traditional technique for this integration is based on Kalman filter (KF) with a dedicated inertial sensor module consisting of three orthogonal gyros and three orthogonal accelerometers. This research targets a low cost navigation solution for land vehicles and hence it utilizes a reduced inertial sensor system (RISS) consisting of MEMS-based single axis gyro and a dual axis accelerometer. Additionally, the vehicle's odometer is used and an integrated 3D navigation solution is achieved. To improve the positioning accuracy a nonlinear filtering technique, particle filter (PF) is used to avoid linearization errors. Because of PF ability to deal directly with nonlinear models, it can accommodate arbitrary sensor characteristics and motion dynamics. Consequently, tightly coupled integration which has a nonlinear measurement model can be directly used in PF without introducing any errors. An enhanced version of PF is implemented known as Mixture PF and the performance of this method is examined by actual road tests in a land vehicle and compared with KF.\",\"PeriodicalId\":94036,\"journal\":{\"name\":\"IEEE/ION Position Location and Navigation Symposium : [proceedings]. IEEE/ION Position Location and Navigation Symposium\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-05-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE/ION Position Location and Navigation Symposium : [proceedings]. IEEE/ION Position Location and Navigation Symposium\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PLANS.2010.5507327\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE/ION Position Location and Navigation Symposium : [proceedings]. IEEE/ION Position Location and Navigation Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PLANS.2010.5507327","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14

摘要

全球定位系统(GPS)、惯性传感器和其他运动传感器集成在陆地车辆内,可以在具有挑战性的GPS环境中实现可靠的定位。在城市峡谷和隧道中,GPS信号可能会受到阻塞,导致定位信息中断。惯性传感器是独立的传感器,可以与GPS集成,可以跨越阻塞期,因为它们不依赖于任何外部信号。近年来,基于微型化微机电系统(MEMS)的惯性传感器广泛应用于安全气囊展开、侧翻检测等车辆安全领域。这些传感器与GPS集成后可作为惯性导航系统(INS)使用,即使在GPS信号缺失的环境下也能提供可靠的导航解决方案。这种集成的传统技术是基于卡尔曼滤波(KF)和由三个正交陀螺仪和三个正交加速度计组成的专用惯性传感器模块。本研究的目标是为陆地车辆提供低成本的导航解决方案,因此它利用了由基于mems的单轴陀螺仪和双轴加速度计组成的简化惯性传感器系统(RISS)。此外,还使用了车辆的里程表,并实现了集成的3D导航解决方案。为了提高定位精度,采用了非线性滤波技术——粒子滤波(PF)来避免线性化误差。由于PF能够直接处理非线性模型,它可以适应任意传感器特性和运动动力学。因此,具有非线性测量模型的紧密耦合积分可以直接用于PF,而不会引入任何误差。采用了一种增强的PF方法,称为混合PF方法,并通过陆地车辆的实际道路试验对该方法的性能进行了检验,并与KF方法进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Nonlinear filtering for tightly coupled RISS/GPS integration
The integration of Global Positioning System (GPS), inertial sensors and other motion sensors inside land vehicles enable reliable positioning in challenging GPS environments. GPS signals may suffer from blockage in urban canyons and tunnels resulting in interrupted positioning information. Inertial sensors are standalone sensors that can be integrated with GPS and can bridge the blockage periods as they do not rely on any external signals. Recently, miniaturized Micro-Electro-Mechanical Systems (MEMS)-based inertial sensors are abundantly used for vehicle safety applications such as air-bag deployment, roll-over detection, etc. These sensors can be used as inertial navigation system (INS) after integrating with GPS for reliable navigation solution even in denied GPS signal environments. The traditional technique for this integration is based on Kalman filter (KF) with a dedicated inertial sensor module consisting of three orthogonal gyros and three orthogonal accelerometers. This research targets a low cost navigation solution for land vehicles and hence it utilizes a reduced inertial sensor system (RISS) consisting of MEMS-based single axis gyro and a dual axis accelerometer. Additionally, the vehicle's odometer is used and an integrated 3D navigation solution is achieved. To improve the positioning accuracy a nonlinear filtering technique, particle filter (PF) is used to avoid linearization errors. Because of PF ability to deal directly with nonlinear models, it can accommodate arbitrary sensor characteristics and motion dynamics. Consequently, tightly coupled integration which has a nonlinear measurement model can be directly used in PF without introducing any errors. An enhanced version of PF is implemented known as Mixture PF and the performance of this method is examined by actual road tests in a land vehicle and compared with KF.
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
期刊最新文献
Innovative Multicarrier Broadband Waveforms for Future GNSS Applications - A System Overview Inertial Navigation on Extremely Resource-Constrained Platforms: Methods, Opportunities and Challenges. Doppler Processing for Satellite Navigation Q-Learning Model Covariance Adaptation of Rao-Blackwellized Particle Filtering in Airborne Geomagnetic Navigation Research on multi-model adaptive hull deformation measurement algorithm
×
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