基于BP神经网络的小样本星图识别方法

Recognition algorithm for star pattern of little swatch based on BP neural network

为解决星敏感器的星图识别算法高实时性和鲁棒性,采用了BP神经网络;根据飞行轨迹精简了导航星及对应的模式,即一个视场中的一幅星图对应一个惟一的导航模式。采用二维Voronoi图计算向平面上最大空圆,构造了完备的圆视场集合;经过反复比较,选择以恒星为顶点能构造包含视场中所有星的凸多边形的导航模式,以其角距和顶角作为识别向量,具有平移和旋转不变性,并以该模式为BP神经网络的训练样本。仿真试验表明:该方法的识别成功率达100%,识别时间小于20ms。

BP neural network is used to improve real-time and robustness for star pattern recognition algorithm of star sensor. According to the trajectory, the navigation stars and corresponding pattern were reduced, that is, the star chart in FOV （field of view） corresponds to one and only navigation pattern. The largest empty circle on plane is computed to construct the exhaustive set in FOV by two-dimensional Voronoi diagram, and then the exhaustive set is constructed. By iterative comparisons, it is found that the navigation pattern of all the stars＇ convex polygon in FOV can be composed if the fixed stars are chosen as the convexes, and taking its angle distance and vertex angle as the recognition vectors has the advantages of translation and rotational invariance. The simulation experiment shows that the success rate of accurate recognition is one hundred percent and the time of recognition is less than 20 ms. Therefore, the recognition algorithm has a certain utility value.