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在观测星数量较少时,匹配星数和边界距离的判定条件达不到网格法要求,同时由于网格法算法本身的原理局限,造成识别率明显下降。针对此问题提出一种改进的网格算法,在保证稳健性的前提下,有效提高星点识别率。实验结果表明,采用该算法,在星点数量少于10颗的条件下,星点识别率从传统网格算法的95%提高到99%。同时,针对星图中未识别和误识别的星点进行的分析,能够为今后网格识别算法的进一步改进提供帮助。
When the number of observing stars is small, the conditions for determining the number of matching stars and the boundary distance can not meet the requirements of grid method. At the same time, due to the limitation of the principle of the gridding method itself, the recognition rate obviously drops. Aiming at this problem, an improved grid algorithm is proposed, which can effectively improve the star point recognition rate under the premise of ensuring the robustness. The experimental results show that the proposed algorithm can improve the recognition rate of star points from 95% to 99% of the traditional one when the number of stars is less than 10. At the same time, the analysis of unidentified and misidentified star points in the star map can help to further improve the future grid identification algorithm.