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INS/GNSS Integration for Aerobatic Flight Applications and Aircraft Motion Surveying

机译:INS / GNSS集成,用于特技飞行应用和飞机运动测量

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This paper presents field tests of challenging flight applications obtained with a new family of lightweight low-power INS/GNSS ( inertial navigation system/global satellite navigation system ) solutions based on MEMS ( micro-electro-mechanical- sensor ) machined sensors, being used for UAV ( unmanned aerial vehicle ) navigation and control as well as for aircraft motion dynamics analysis and trajectory surveying. One key is a 42+ state extended Kalman-filter-based powerful data fusion, which also allows the estimation and correction of parameters that are typically affected by sensor aging, especially when applying MEMS-based inertial sensors, and which is not yet deeply considered in the literature. The paper presents the general system architecture, which allows iMAR Navigation the integration of all classes of inertial sensors and GNSS ( global navigation satellite system ) receivers from very-low-cost MEMS and high performance MEMS over FOG ( fiber optical gyro ) and RLG ( ring laser gyro ) up to HRG ( hemispherical resonator gyro ) technology, and presents detailed flight test results obtained under extreme flight conditions. As a real-world example, the aerobatic maneuvers of the World Champion 2016 (Red Bull Air Race) are presented. Short consideration is also given to surveying applications, where the ultimate performance of the same data fusion, but applied on gravimetric surveying, is discussed.
机译:本文介绍了使用基于MEMS(微电子机械传感器)机械化传感器的新型轻量级低功耗INS / GNSS(惯性导航系统/全球卫星导航系统)解决方案获得的具有挑战性的飞行应用的现场测试。用于无人机(UAV)导航和控制,以及飞机运动动力学分析和轨迹测量。一个关键是基于42+状态扩展卡尔曼滤波器的强大数据融合,它还可以估计和校正通常受传感器老化影响的参数,尤其是在应用基于MEMS的惯性传感器时,并且尚未得到深入考虑在文学中。本文介绍了通用系统架构,该架构允许iMAR Navigation通过FOG(光纤陀螺仪)和RLG(非常低成本的MEMS和高性能MEMS)集成所有类别的惯性传感器和GNSS(全球导航卫星系统)接收器。高达HRG(半球谐振陀螺仪)技术的环形激光陀螺仪,并展示了在极端飞行条件下获得的详细飞行测试结果。举一个真实的例子,介绍了2016年世界冠军(Red Bull Air Race)的特技飞行动作。还简要考虑了测量应用程序,在该应用程序中讨论了相同数据融合但应用于重力测量的最终性能。

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