高动态星跟踪方法

阐述了高动态星敏感器星图的特点，指出了目前星跟踪方法的不足。针对这些不足，提出了一种基于卡尔曼预测的高动态星跟踪方法。根据高动态星敏感器运动特性，建立了星体目标在图像坐标系下运动模型，根据星体运动模型，对卡尔曼滤波器进行了自适应修正。利用经自适应修正的卡尔曼滤波器预测出参考星位置，再利用临星逼近法进行跟踪匹配。最后给出了利用上述方法进行星体位置预测及星跟踪结果。实验结果表明，在5（）/s动态条件下星体位置预测偏差小于5像素，星跟踪成功率高于95%，并且载体动态特性的变化对星体跟踪成功率影响较小。

Highly dynamic star tracking algorithm

The character of high dynamic star sensor's sky image and the deficiency of existing star tracking algorithm at home and abroad were presented. Aiming at these deficiencies, a new star tracking algorithm based on Kalman prediction was put forward. The model of stars' movement was set up based on the character of the star sensor's movement. The adaptive Kalman filter was used to predict the position of the reference stars. The star was matched and tracked by Star Neighborhood Approach. At the end of the article, the prediction and tracking results were presented. The experiment results indicate that the star position prediction errors are less than 5 pixels under the dynamic condition of 5()/s, and the success rate of tracking is up to 95%. The method can adapt for high dynamic star sensor and improve the success rates of tracking availably.