摘要
姿态解算是小型多旋翼无人机研究的核心问题之一。为进一步提高姿态反馈数据的准确度和实时性,设计了低成本姿态解算系统,根据不同姿态传感器的信号特点,结合卡尔曼滤波融合算法和改进型互补滤波融合算法的优势,提出了一种基于前置卡尔曼滤波器的互补滤波融合姿态解算方法,并进行了飞行姿态解算实验。结果表明,代码的运行周期比改进型互补滤波融合算法增加了0.253 ms,比基于加权滑动方差的卡尔曼滤波融合算法减少了0.246 ms;姿态更新时间提前了约1 ms。悬停状态下,俯仰角和翻滚角的解算误差控制在±0.2°以内;航向角的解算误差控制在±0.6°以内。该方法集合了卡尔曼滤波算法和互补滤波算法的优势,准确度更高,实时性更强。
Attitude calculation is a key subject of flight control technology. By analyzing the data of different attitude sensors, the noise signal characteristics of accelerometers, magnetometers and gyroscopes were found out.By comparing the functions of different filters, a data fusion algorithm of attitude estimation was proposed based on pre-Kalman filter and complementary filter, which could improve the performance of the two filters and had both of their advantages. The experimental results showed the code operation cycle was by 0. 253 ms longer than that of the modified complementary filtering fusion algorithm, and by 0. 246 ms less than the Kalman filter fusion algorithm based on variance weighted sliding, but the attitude data update time is about 1 ms earlier.Furthermore under the drones hover the calculation error ranges of both pitch angle and roll angle were within±0. 2°, that range of yaw angle was within ± 0. 6°. This attitude calculation method provides more accurate feedback data for flight attitude control and has better real-time performance than other methods.
作者
张春友
龙长江
吴伟
严炜
ZHANG Chun-you;LONG Chang-jiang;WU W ei;YAN W ei(College of Engineering, Huazhong Agricultural University, Wuhan 430070, Chin)
出处
《测控技术》
CSCD
2018年第2期47-50,共4页
Measurement & Control Technology
基金
中央高校基本科研业务费专项基金资助项目(2014JC001)
关键词
卡尔曼滤波
互补滤波
姿态解算
无人机
Kalman filtering
complementary filtering
attitude calculation
UAV
