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GPS Fault Detection with IMU and Aircraft Dynamics

机译:具有IMU和飞机动力学的GPS故障检测

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Approaches with vertical guidance (APV) can provide greater safety and cost savings to general aviation through accurate GPS horizontal and vertical navigation. However, GPS needs augmentation to achieve APV fault detection (FD) requirements. Aircraft-based augmentation systems (ABAS) fuse GPS with additional sensors at the aircraft. Typical ABAS designs assume high-quality inertial sensors with Kalman filters but these are too expensive for general aviation. Instead of using high-quality (and expensive) sensors, the purpose of this paper is to investigate augmenting GPS with a low-quality micro electro-mechanical system (MEMS) inertial measurement unit (IMU) and aircraft dynamic model (ADM). The IMU and ADM are fused together using a multiple model fusion strategy in a bank of extended Kalman filters (EKF) with the normalized solution separation (NSS) FD scheme. A tightly-coupled configuration with GPS is used and frequent GPS updates are applied to the IMU and ADM to compensate for their errors. Based upon a simulated APV approach, the performance of this architecture in detecting a GPS ramp fault is investigated, showing a performance improvement over a GPS-only "snapshot" implementation of the NSS method. The effect of fusing the IMU with the ADM is evaluated by comparing a GPS-IMU-ADM EKF with a GPS-IMU EKF, where a small improvement in protection levels is shown.
机译:垂直导航(APV)的方法可以通过精确的GPS水平和垂直导航为通用航空提供更大的安全性和成本节省。但是,GPS需要增强以实现APV故障检测(FD)要求。基于飞机的增强系统(ABAS)将GPS与飞机上的其他传感器融合在一起。典型的ABAS设计假定带有Kalman滤波器的高质量惯性传感器,但对于通用航空而言,它们太昂贵了。代替使用高质量(昂贵)的传感器,本文的目的是研究使用低质量的微机电系统(MEMS)惯性测量单元(IMU)和飞机动力学模型(ADM)来增强GPS。使用多模型融合策略将IMU和ADM融合在一起,并使用标准化解决方案分离(NSS)FD方案在一组扩展的卡尔曼滤波器(EKF)中融合在一起。使用与GPS紧密耦合的配置,并将频繁的GPS更新应用于IMU和ADM以补偿其错误。基于模拟的APV方法,研究了该体系结构在检测GPS斜坡故障中的性能,显示出比NSS方法的仅GPS“快照”实现更好的性能。通过将GPS-IMU-ADM EKF与GPS-IMU EKF进行比较,可以评估将IMU与ADM融合的效果,其中显示出保护水平的小幅提高。

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