一种基于Madgwick-EKF融合算法的卫星姿态测量方法
A Satellite Attitude Measurement Method Based on the Madgwick-EKF Fusion Algorithm
史炯锴 1张松勇 1渐开旺 1高迪驹1
作者信息
- 1. 上海海事大学 航运技术与控制工程交通运输行业重点实验室,上海 201306
- 折叠
摘要
针对低地球轨道卫星姿态测量时,传感器易受噪声干扰、陀螺仪漂移等问题,提出一种基于Madgwick扩展卡尔曼滤波合算法(EKF)的卫星姿态测量方法.该方法采用陀螺仪、加速度计、磁强计等多传感器数据进行融合,并结合Madgwick算法和EKF算法的优点,实现姿态测量.首先,通过Madgwick算法,利用多个传感器测量数据计算初始姿态.然后,基于初始姿态和实际测量数据,应用EKF算法进行数据融合和噪声滤除,以获得最终准确的姿态估计.实验结果表明:相较Madgwick算法,本算法在测量精度上提升了65.8%,且具有较高的鲁棒性,为低地球轨道卫星姿态测量提供了一种有效的方案.
Abstract
In response to the issues such as sensor noise interference and gyroscope drift during the attitude measurement of low Earth orbit(LEO)satellites,a satellite attitude measurement method based on the Madgwick-extended Kalman filter(EKF)fusion algorithm is proposed.This method uses the data of multiple sensors,e.g.,gyroscopes,accelerometers,and magnetometers,for fusion,and leverages the advantages of both the Madgwick algorithm and the EKF algorithm to achieve attitude measurement.Initially,the Madgwick algorithm is used to calculate the initial attitude with the data measured by multiple sensors.Subsequently,based on the initial attitude and the measured data,the EKF algorithm is used for data fusion and noise filtering so as to obtain the final accurate attitude estimation.The experimental results indicate that compared with the Madgwick algorithm,the fusion algorithm improves the measurement accuracy by 65.8%,and exhibits high robustness.This method provides an effective solution for the attitude measurement of LEO satellites.
关键词
姿态测量/姿态传感器/Madgwick算法/扩展卡尔曼滤波/近地轨道卫星Key words
attitude measurement/attitude sensor/madgwick algorithm/extended kalman filter(EKF)/low earth orbit satellite引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据