为了建立更高效的星图识别算法,设计了一种以主星与邻星角距为识别特征的星图识别算法。构建了导航星库,将所有邻星与主星角距进行量化编码,并按照从小到大的顺序排列为特征模式字符串,串尾附加字符串长度项为聚簇索引,借以提高导航星库的检索速度;然后,利用改进的KMP字符串识别方法对观测星进行匹配识别。当存在2 pixels的高斯位置噪声时,该算法成功识别率高于97.38%,导航星库的存储容量为149.4 kB。在Pentium 1.6 GHz PC机上随机识别1 250幅星图,平均识别时间为42.78 ms。此识别算法对观测图像具有旋转不变性,对位置和星等噪声都有很好的鲁棒性,且导航星库的存储容量小、检索效率高、实时性好。

To establish a more effective string pattern matching algorithm, a new improved star identification algorithm taking angular distances between main star and neighbor stars as recognition characteristics is designed. A guide star catalogue is constructed,and the angular distance values between main star and neighbor stars are encoded to quantized codes; then each code in characteristic character strings is rearranged from small to large in sequence. A string length code is appended to the end of the string to improve search speed of guide star catalogue, and so far the main star’s neighbor pattern is finished. Finally, an improved string matching algorithm based on KMP is proposed to identify the corresponding matches to the measured star in the whole guide star patterns. The identification rate of this algorithm is greater than 97.38% when the Gauss noise of position error is 2 pixels. 1 250 star maps are identified randomly on Pentium 1.6 GHz PC,and the average identification time is 42.78 ms. This algorithm has rotation invariability, good robustness and synchronization. The catalogue storage content is only 149.4 kB,so it can offer a higher retrieval efficiency.