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针对传统无人机姿态解算方法过程复杂、计算量大、动态性能差的缺点,建立无人机姿态模型;采用陀螺仪对加速度计直接进行滤波的方法,设计出新的基于扩展kalman滤波的加速度滤波器;并且考虑到无人机非重力加速度的影响,对常规kalman滤波器进行了变噪声的改进。利用STM32微控制器和MEMS惯性单元搭建硬件平台进行对比实验。结果表明:在168 MHz时钟频率下,一次传感器数据读取和姿态解算总共耗时3.27 ms,数据更新率可达100 Hz。新算法飞行动态误差小于1°,而传统四元数法动态误差为2°左右;变噪声处理后静态瞬时偏差由4°降到1°。说明新算法的抗震效果和解算精度更好,可以为无人机自主飞行提供更准确的姿态信息。
Aiming at the shortcomings of traditional UAV attitude solving methods, such as complex process, large amount of calculation and poor dynamic performance, a UAV pose model is established. The gyroscope is used to filter the accelerometer directly, and a new algorithm based on extended kalman filtering Acceleration filter; and taking into account the impact of non-gravitational acceleration UAV, the conventional Kalman filter noise-improved. Use STM32 microcontroller and MEMS inertial unit to build a hardware platform for comparative experiments. The result shows that the reading and attitude calculation of sensor data takes a total time of 3.27 ms and the data updating rate can reach 100 Hz at a clock frequency of 168 MHz. The dynamic error of the new algorithm is less than 1 °, while the dynamic error of the traditional quaternion method is about 2 °. The static instantaneous deviation after the noise is reduced from 4 ° to 1 °. It shows that the new algorithm has better seismic performance and better solution accuracy and can provide more accurate attitude information for UAV autonomous flight.