Tightly coupled navigation and wind estimation for mini UAVs
Tightly coupled navigation and wind estimation for mini UAVs
复制标题
微型无人机的紧耦合导航和风估计
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
S. Bonnabel
中科院分区：
文献类型：
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作者：
Martin Brossard;Jean;S. Bonnabel
提出了一种基于辅助惯导系统、一维皮托管、攻角和侧滑角传感器的状态空间模型非线性滤波器。该方案基于乘性卡尔曼滤波，在传感器偏差和三维风分量的情况下，实时估计无人机的方位、速度和位置沿着。本文描述和证明设计的紧耦合估计方案的理论和实验的考虑。然后，我们验证了整个方法的微型无人机控制通过著名的狗仔队自动驾驶仪系统，我们配备了一套低成本的传感器（加速度计，陀螺仪，GPS，磁力计，气压计，一维皮托管静态和角度传感器），通过成功地比较从真实的飞行数据获得的估计与3D风地面实况提供了一个60米的气象测量塔。
A novel nonlinear filter for the state-space model based on aided Inertial Navigation System, 1D Pitot static tube, angle-of-attack and angle-of-sideslip sensors is proposed. The solution, based on Multiplicative Kalman filtering, estimates in real-time the orientation, the velocity and the position of an Unmanned Aerial Vehicle along with sensor bias and 3D wind components. This paper describes and justifies the designed tightly coupled estimation scheme with both theoretical and experimental considerations. We validate then the whole approach for a mini UAV controlled through the well-known Paparazzi autopilot system which we equip with a set of low-cost sensors (accelerometers, gyros, GPS, magnetometer, barometer, 1D Pitot static tube and angular sensors), by successfully comparing the estimates obtained from real flight data with the 3D wind ground truth provided from a 60-m weather measurement tower.