通过反向姿态统计提升星图识别准确率，解决缺星误星问题。
Reverse Attitude Statistics Based Star Map Identification Method
- 将姿态求解融入匹配过程，利用频率统计同步获得匹配结果与正确姿态。
- 仿真与在轨实验显示识别率提升超14.3%，求解时间减少28.5%以上。
- 适合高动态、低信噪比环境下星跟踪器使用，尤其适用于近空间任务。
星图识别在近空间工作时易受大气背景光和气动环境影响，导致缺星或误星，高速机动还可能引起星迹，降低星位精度。为应对这些挑战，本文提出一种基于反向姿态统计的星图识别方法。与传统先匹配后求姿态的方法相反，该方法将姿态求解引入匹配过程，通过频率统计同时获得最终匹配结果与正确姿态。首先基于稳定角距特征，利用空间哈希索引获取初始匹配；随后引入双矢量姿态确定法计算潜在姿态；最后通过频率统计滤波实现星对精准匹配。此外，采用贝叶斯优化在噪声影响下寻找最优参数，进一步提升算法性能。所提方法在仿真、外场测试及在轨实验中验证，相比现有先进方法，识别率提升超过14.3%，求解时间缩短28.5%以上。
原文摘要 · Abstract (English)
The star tracker is generally affected by the atmospheric background light and the aerodynamic environment when working in near space, which results in missing stars or false stars. Moreover, high-speed maneuvering may cause star trailing, which reduces the accuracy of the star position. To address the challenges for starmap identification, a reverse attitude statistics based method is proposed to handle position noise, false stars, and missing stars. Conversely to existing methods which match before solving for attitude, this method introduces attitude solving into the matching process, and obtains the final match and the correct attitude simultaneously by frequency statistics. Firstly, based on stable angular distance features, the initial matching is obtained by utilizing spatial hash indexing. Then, the dual-vector attitude determination is introduced to calculate potential attitude. Finally, the star pairs are accurately matched by applying a frequency statistics filtering method. In addition, Bayesian optimization is employed to find optimal parameters under the impact of noises, which is able to enhance the algorithm performance further. In this work, the proposed method is validated in simulation, field test and on-orbit experiment. Compared with the state-of-the-art, the identification rate is improved by more than 14.3%, and the solving time is reduced by over 28.5%.
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