结合自组织映射网络及三角形算法的星图识别方法

三角形方法是最经典且应用最广的星图识别方法之一，但是存在搜索范围大、匹配冗余、抗噪能力弱等问题。将神经网络技术应用到星图识别过程中，结合自组织映射网络（SOM）优秀的分类能力和三角形算法可靠的角距匹配能力，提出了一种新的识别方法。该方法基于邻近星的分布来构建每颗导航星的特征向量，将其作为SOM网络的输入向量，通过训练得到具有分类识别功能的网络及相应的三角形库。识别阶段，输入待识别星的特征向量，网络输出识别类，在该类对应的三角形库中应用三角形算法查找匹配三角形，完成星图识别。试验发现该方法减小三角形搜索范围、实现快速匹配的同时，提高了识别系统的抗噪能力，在全天识别过程中平均识别时间低于5ms，识别率在噪声标准差为0.025时仍高达99%。

A star pattern recognition method based on self-organizing map network and triangle algorithm

Triangle algorithm is one of the most classical and widely used method of star pattern identification, but it has short comings of large search range, redundancy match, weak anti-noise ability, and so on. Applying the neural network technology to the satellite image recognition, a new recognition method combined with theexcellent classification ability of neural network technology and the reliable angular matching ability of the triangle algorithm was proposed. The feature vector sofeachguidestar constructed based on the distribution of the neighboring stars were used to train the SOM network of the star pattern recognition system so that the classification function was obtained. The network could identify the class of the unknown star by the feature vector. In the corresponding triangle database, the triangle algorithm was applied to find the matching triangle and identify the star map. Results of simulated identification show that thenew method can reduce the star catalog, identify the star map fast and make the system more robust with respect to noise. In the whole sky identification, the average recognition time is lessthan 5ms, and the recognition rate is still as high as 99% when the noise standard deviation is 0.025.