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首页> 外文会议>International Conference on Recent Advances in Space Technologies >Two-Stage Kalman Filter for Estimation of Wind Speed and UAV Flight Parameters based on GPS/INS and Pitot Tube Measurements
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Two-Stage Kalman Filter for Estimation of Wind Speed and UAV Flight Parameters based on GPS/INS and Pitot Tube Measurements

机译：基于GPS / INS和皮托管的两阶段卡尔曼滤波器估计风速和无人机飞行参数

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Two-stage Kalman filter based estimation algorithm was developed for wind speed and UAV motion parameters. In the first stage., wind speed estimation algorithm is used based on GPS measurements and dynamic pressure measurements. The wind speed is estimated by Kalman filter using GPS and Air Data System (ADS)measurements. For this purpose., Extended Kalman Filter (EKF)was designed., and as state variables., the wind velocity components and ADS pitot scale factor are considered. In the second stage., the state parameters of the UAV dynamic model are estimated using GPS and IMU measurements. The second stage filter uses GPS position measurements., IMU orientation angles and speed measurements as well as the wind speed value estimated by the first stage filter.
机译：针对风速和无人机运动参数，开发了基于两阶段卡尔曼滤波器的估计算法。在第一阶段，基于GPS测量值和动态压力测量值使用风速估计算法。风速通过卡尔曼滤波器使用GPS和空中数据系统（ADS）测量来估算。为此，设计了扩展卡尔曼滤波器（EKF），并考虑了风速分量和ADS皮托管比例因子作为状态变量。在第二阶段，使用GPS和IMU测量值估计无人机动态模型的状态参数。第二级滤波器使用GPS位置测量值，IMU方位角和速度测量值以及第一级滤波器估计的风速值。

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