刘宇, 杨晓辉, 郭俊启, 钟懿, 刘洪志.一种基于AIKF的姿态测量算法[J].压电与声光,2018,40(3):454-459.LIU Yu, YANG Xiaohui, GUO Junqi, ZHONG Yi, LIU Hongzhi.An Attitude Measurement Algorithm Based on AIKF[J].PIEZOELECTRICS AND ACOUSTOOPTICS
一种基于AIKF的姿态测量算法
An Attitude Measurement Algorithm Based on AIKF
  
DOI:10.11977/j.issn.1004-2474.2018.03.033
中文关键词:  姿态测量  自适应增量卡尔曼滤波  互补滤波  自适应因子  数据融合
英文关键词:attitude measurement  adaptive incremental Kalman filter  complementary filter  adaptive factor  data fusion
基金项目:国家自然科学基金资助项目(61301124,61471075,61671091);重庆市科委基础研究资助项目(cstc2014jcyjA1350);重庆邮电大学博士启动基金资助项目(A2015 40);重庆科委自然科学基金资助项目(cstc2016jcyjA0347)
作者单位
刘宇, 杨晓辉, 郭俊启, 钟懿, 刘洪志 (重庆邮电大学 重庆市光电信息感测与传输技术重点实验室重庆 400065) 
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中文摘要:
      惯性测量单元中传感器具有较强的非线性和噪声的不确定性,导致使用常规卡尔曼滤波时误差大,容易出现发散,针对此问题,该文提出了一种改进的自适应增量卡尔曼滤波(AIKF)算法。该算法使用互补滤波将加速度计、磁力计和陀螺仪的数据进行融合,利用滤波后的数据增量作为卡尔曼滤波器的观测量,同时对系统噪声进行自适应在线估计,以获得精准的姿态输出。实验结果表明,该算法能够实现姿态的精准测量,摇摆台试验中俯仰角、横滚角误差小于0.05°,航向角误差小于0.15°,具有较好的噪声抑制能力。
英文摘要:
      The sensors in the inertial measurement unit have strong nonlinearity and noise uncertainty, resulting in large errors and divergence when using the conventional Kalman filtering. In order to solve this problem, an improved adaptive incremental Kalman filter (AIKF) algorithm is proposed in this paper. The algorithm uses complementary filtering to fuse the data from accelerometers, magnetometers and gyroscopes, and the filtered data increment is used as the observation of the Kalman filter. At the same time, the adaptive on line estimation of the system noise is carried out to obtain accurate attitude output. The experimental results show that the algorithm can achieve accurate measurement of attitude, the errors of the pitch angle and roll angle are less than 0.05° and the heading error is less than 0.15° with the rolling table test, and it has a better ability to suppress the noise.
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