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针对现有星跟踪算法依赖外部信息、模型复杂等缺点,结合卫星平台实际,提出了一种新的星敏感器内部星跟踪算法,利用星矢量与角速率间关系进行星点预测。先根据当前帧和前一帧的星矢量,用最小二乘原理估算出角速率,再对角速率进行卡尔曼滤波以减小估计误差,之后用滤波后的角速率预测下一帧的星矢量,将星矢量转为星点坐标,最后在以该坐标为中心的波门中提取星点。讨论了角速率估计误差、截断误差、星点成像随机误差和定姿星数等因素对算法精度的影响。该方法不依赖外部信息进行预测,模型简单,易于实现。实验验证在星敏感器角速率为0.6(°)/s时,平均跟踪精度0.55像素,证明了其有效性。
In view of the shortcomings that the existing star tracking algorithm relies on external information and the model is complex, a new star sensor internal star tracking algorithm is proposed based on the actual satellite platform. The star vector prediction is based on the relationship between the star vector and the angular rate. First, based on the star vector of the current frame and the previous frame, the angular rate is estimated by the principle of least squares, and the angular rate is Kalman filtering to reduce the estimation error. Then the star vector of the next frame is predicted by the filtered angular rate , The star vector into the star coordinates, and finally in the coordinates of the center of the gate to extract the star. The effects of angular velocity estimation error, truncation error, star point imaging stochastic error and number of stationary stars on the accuracy of the algorithm are discussed. The method does not rely on external information to predict, the model is simple and easy to implement. Experimental results show that the average tracking accuracy is 0.55 pixels when the angular velocity of the star sensor is 0.6 (°) / s, which proves the validity.