Robust state estimation for small unmanned airplanes
Robust state estimation for small unmanned airplanes
复制标题
小型无人机的鲁棒状态估计
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
R. Siegwart
中科院分区：
文献类型：
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作者：
Stefan Leutenegger;A. Melzer;K. Alexis;R. Siegwart
作为自主运行的基础，无人机要求星载状态估计在一定条件下具有较高的精度和稳健性。我们提出了一种基于扩展卡尔曼滤波(EKF)的多传感器融合框架，该框架重量轻，可以使用基于MEMS的惯性测量单元(IMU)、静态和动态压力传感器以及GPS(位置和速度)和3D磁罗盘的测量结果在小型无人机上运行。机载状态估计器以紧密耦合的方式连续估计位置、速度、姿态和航向、IMU偏差以及3D风矢量。此外，飞机的迎角和侧滑角也可以通过空气动力学模型得到。由此产生的基础设施允许无偏定位、空速和AOA跟踪，即使在GPS长时间停机的情况下也是如此。它还可以使用马哈拉诺比斯距离检查来检测和拒绝传感器读数中的异常值。我们用一架小型无人机的飞行数据验证了所提出的方法：我们证明了该方法的鲁棒性。在后处理分析中禁用或损坏各自的飞行数据，从而导致离群值和GPS中断。
As a basis for autonomous operation, Unmanned Aerial Systems (UAS) require an on-board state estimation that achieves both high accuracy as well as robustness with respect to certain conditions. We present a multi-sensor fusion framework based on Extended Kalman Filtering (EKF) which is light-weight enough to run on-board small unmanned airplanes using measurements from a MEMS based Inertial Measurement Unit (IMU), static and dynamic pressure sensors, as well as GPS (position and velocity) and a 3D magnetic compass. The on-board state estimator continuously estimates position, velocity, attitude and heading, IMU biases as well as the 3D wind vector in a tightly-coupled manner. In addition, airplane Angle of Attack (AoA) as well as sideslip angle can be derived by involving an aerodynamics model. The resulting infrastructure allows for unbiased orientation, airspeed and AoA tracking even in the case of GPS outages over extended periods of time. It can furthermore detect and reject outliers in sensor readings using Mahalanobis distance checks. We validate the proposed method with flight data from a small unmanned airplane: we demonstrate robustness w.r.t. outliers and GPS outages by disabling or corrupting respective flight data in post processing analyses.