添加补偿码的快速径向伴星特征星图识别

发布时间：2018-06-06 12:30

  本文选题：星图识别 + 补偿码　； 参考：《光学精密工程》2017年06期

【摘要】：针对传统的基于径向特征的星图识别算法在构建星模式的过程中由于位置噪声的干扰导致识别率较低的问题,本文提出一种添加补偿码的快速径向伴星星图识别算法。该算法以比特向量的形式构建基于径向特征的特征向量,同时将伴星间的角距信息以及位置噪声的补偿信息添加到特征向量中,从而有效地减小了特征库的容量,提高了星图识别算法的稳定性和识别率。最后本文根据比特向量的特点采用最小相似差方法快速完成观测星与导航星之间的初匹配,再根据同一视场内星点位置信息的相关性完成对观测星的唯一识别。实验仿真结果表明,在位置噪声为0.5像素的情况下星图识别成功率达到97.8%;在星等噪声为0.8 Mv的情况下星图识别成功率达到96.4%;当以真实星图为实验对象时,星图识别的成功率达到94.2%。与传统的三角形算法以及未添加补偿码的径向特征星图识别算法相比,本文算法在识别成功率和识别时间上均有着不同程度的提高。
[Abstract]:In order to solve the problem of low recognition rate due to the interference of position noise in the traditional star pattern recognition algorithm in the process of building star pattern, a fast radial companion star map recognition algorithm with compensation code is proposed in this paper. The angular distance information between the companion stars and the compensation information of the position noise are added to the feature vector, which effectively reduces the capacity of the feature library and improves the stability and recognition rate of the star pattern recognition algorithm. Finally, according to the characteristics of the bit vector, the minimum similarity difference method is used to quickly complete the initial match between the observation star and the navigation star. The only recognition of the observational stars is completed according to the correlation of the location information in the same field of view. The experimental simulation results show that the star map recognition success rate reaches 97.8% when the position noise is 0.5 pixels, and the star map recognition success rate reaches 96.4% when the star noise is 0.8 Mv; when the real star map is used as the experimental object, the star map recognition is made. Compared with the traditional triangle algorithm and the radial feature map recognition algorithm without compensation code, the success rate of 94.2%. is improved in different degrees in recognition success rate and recognition time.
【作者单位】： 中国科学院长春光学精密机械与物理研究所;
【基金】：国家863高科技研究发展计划(No.2011AAxx2035)
【分类号】：P128
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本文编号：1986521