为了实现星敏感器对航天器当前姿态的准确测量, 如何提高星图识别算法的实时性和鲁棒性成为星敏感器的关键技术。对星图识别过程中应用的模式提取、训练样本集的建立以及神经网络训练方式的改进等算法进行研究。首先, 设计一种基于星图特征的三角形剖分方法, 将视场内的恒星以三角形的方式组合起来, 提取星图模式, 建立完备的训练样本集, 使星图特征具有平移和旋转不变性。然后, 采用 BP神经网络识别星图特征, 以权值矩阵代替导航星库, 一旦网络训练完成, 可以很快获得当前星图信息, 实现星敏感器星图识别算法的实时性和鲁棒性; 为了优化 BP神经网络改进其自身缺点, 采用 PSO(粒子群算法 )训练 BP神经网络, 获取使 BP神经网络趋近全局最优的初始权值和阈值, 使其加快收敛至全局最优。由实验结果表明, 该星图识别算法识别率达 100%。

In order to realize accurate measurement of aircraft’s current attitude, how to improve real time and robustness of star pattern recognition is the key of star sensor. The algorithms for star pattern abstraction, training sample set creation and network training improvement are proposed. First, a method of triangulation based on the character of star image is designed to combine all the stars of current field of view, which is used to extract star pattern and create complete training samples. The character of star pattern extracted has the advantages of translation and rotation invariance. Then BP Neural Network serves to recognize the star pattern with the weight matrix instead of navigation library. It is very fast to acquire current star information when the network has finished training. Particle Swarm Optimization (PSO) serves to train BP Neural Network, which helps BP network converge to the most optimum value. The experimental results show that the success rate of accurate recognition is 100%.