Abstract In this study, an estimation algorithm based on a two-stage Kalman filter (TSKF) was developed for wind speed and Unmanned Aerial Vehicle (UAV) motion parameters. In the first stage, the wind speed estimation algorithm is used with the help of the Global Positioning System (GPS) and dynamic pressure measurements. Extended Kalman Filter (EKF) is applied to the system. The state vector is composed of the wind speed components and the pitot scale factor. In the second stage, in order to estimate the state parameters of the UAV, GPS, and Inertial Measurement Unit (IMU) measurements are considered in a Linear Kalman filter. The second stage filter uses the first stage EKF estimates of the wind speed values. Between these two stages, a sensor fault detection algorithm is placed. The sensor fault detection algorithm is based on the first stage EKF innovation process. After detecting the fault on the sensor measurements, the state parameters of the UAV are estimated via robust Kalman filter (RKF) against sensor faults. The robust Kalman filter algorithm, which brings the fault tolerance feature to the filter, secures accurate estimation results in case of a faulty measurement without affecting the remaining good estimation characteristics. In simulations, noise increment and bias type of sensor faults are considered.

中文翻译：

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用于风速和无人机飞行参数容错估计的两级卡尔曼滤波器

摘要 在这项研究中，开发了一种基于两级卡尔曼滤波器（TSKF）的风速和无人机（UAV）运动参数估计算法。在第一阶段，在全球定位系统 (GPS) 和动态压力测量的帮助下使用风速估计算法。扩展卡尔曼滤波器 (EKF) 应用于系统。状态向量由风速分量和皮托管比例因子组成。在第二阶段，为了估计无人机的状态参数，在线性卡尔曼滤波器中考虑 GPS 和惯性测量单元 (IMU) 测量。第二级滤波器使用第一级 EKF 对风速值的估计。在这两个阶段之间，放置了传感器故障检测算法。传感器故障检测算法基于第一阶段EKF创新过程。在检测到传感器测量故障后，无人机的状态参数通过针对传感器故障的鲁棒卡尔曼滤波器 (RKF) 进行估计。鲁棒的卡尔曼滤波器算法将容错特性引入滤波器，在测量错误的情况下确保准确的估计结果，而不会影响剩余的良好估计特性。在仿真中，考虑了传感器故障的噪声增量和偏差类型。在测量错误的情况下确保准确的估计结果，而不会影响剩余的良好估计特性。在仿真中，考虑了传感器故障的噪声增量和偏差类型。在测量错误的情况下确保准确的估计结果，而不会影响剩余的良好估计特性。在仿真中，考虑了传感器故障的噪声增量和偏差类型。