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Spinning missile attitude estimation method based on the solar vector and MEMS

机译:基于太阳矢量和MEMS的自旋导弹姿态估计方法

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In this paper, a new method of estimating the attitude to spinning missile was proposed. The author designed the method of using solar vector to correct the gyro bias of MEMS sensor and using the Kalman filter algorithm to estimate the spinning missile attitude. According to the nature of spinning missile, this paper designed a special sun sensor that is used to measure the solar vector replaced the commonly used in MEMS sensor of accelerometer and magnetometer to correct the gyro bias of MEMS sensor. The solar vector measuring device comprises the transparent shell, the photocell array, the real-time clock and the controller. When the sun shines through the transparent shell illuminates the photocell array, the photocell array can convert the solar energy into the electric potential signal. According to the size of the solar energy and the angle of the photocell laying, combining with the accurate time provided by the real time clock, the solar vector of this time is obtained through the main controller processes data. The Kalman filer state equation and observation equation established by the quaternion differential equation is used to estimate the spinning missile attitude. The simulation results show that this method can obtain the higher accuracy attitude angle of spinning missile.
机译:本文提出了一种估计自旋导弹姿态的新方法。作者设计了利用太阳矢量来校正MEMS传感器的陀螺偏差并使用卡尔曼滤波算法来估计自旋导弹姿态的方法。根据旋转导弹的性质,设计了一种用于测量太阳矢量的特殊太阳传感器,代替了加速度计和磁力计中常用的MEMS传感器,以校正MEMS传感器的陀螺偏差。太阳矢量测量装置包括透明壳体,光电管阵列,实时时钟和控制器。当阳光透过透明外壳照亮光电管阵列时,光电管阵列可以将太阳能转换为电位信号。根据太阳能的大小和光电池的放置角度,结合实时时钟提供的准确时间,可以通过主控制器处理数据获得该时间的太阳能矢量。由四元数微分方程建立的卡尔曼滤波器状态方程和观测方程用于估计自旋导弹的姿态。仿真结果表明,该方法可以获得较高的旋转导弹姿态角精度。

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