随着当前星敏感器视场（FOV）的增大，探测能力的提高，一帧图中拍摄到的恒星更多。但是受星敏感器光谱范围的限制及空间环境干扰影响，星等测试精度一般不高于0.2 mV。为了充分发挥当前星敏感器视场和探测能力的优势，并避免星等误差的影响，提高全天区星图识别算法在线应用的适用性，提出了一种星点坐标辅助的全天区三角形星图识别算法。该方法采用"全局初步搜索识别—局部精细匹配验证—最优结果选取"的算法思想。首先，根据星敏感器探测到的极限星等范围构建导航星表，选取亮星构建角距星表，既确保了星表的完备性，又有利于充分利用星敏感器的探测能力。然后，在三角形约束条件下进行角距匹配识别，得到一个或多个导航三角形，在该识别环节提出了非线性矢量法查找星表，既提高了定位精度，又能采用单精度数据类型降低存储空间。最后，提出局部天区星点坐标匹配算法进一步消除冗余匹配，同时又识别出视场内更多的观测星，有利于提高识别率和定姿精度。试验结果表明，与其他一些经典的星图识别算法相比，所提算法在识别率和星表容量方面更有优势。识别率可达99.9%，且随着星等的增加，存储容量增加的最少。所提算法更加适于大视场、高星等敏感范围的星敏感器在线应用。
With the increase of the Field of View (FOV) and detection ability of the-state-of-the-art star sensors, more stars can be imaged in one frame of the star sensor. However, due to the limitation of the spectrum range of the star sensor and the influence of space environment, the measuring accuracy of star magnitude is generally not higher than 0.2 mV. In this paper, an all-sky triangle algorithm aided by star coordinates is presented to take full advantage of the FOV and detection capability of the star sensor, avoid the influence of the magnitude error, and also improve applicability of online application of all-sky star identification. The algorithm adopts the idea of "global preliminary recognition-local fine matching-selection of the best result". First, a navigation star catalog is constructed by all the stars that can be detected by the star sensor and bright stars are selected to construct the pair catalogue, which not only ensures the completeness of the star catalog, but also makes full use of the detection ability. Then, star angular distance matching is carried out under the triangle constraint condition, obtaining one or more navigation triangles. To improve the matching speed, a nonlinear vector algorithm is proposed to look up the pair catalogue, which not only improves the positioning accuracy, but also reduces the storage space by using the single-precision data type. Finally, a algorithm for matching the star coordinates in the local sky is proposed to further eliminate redundant matching and identify more stars in the FOV, which is beneficial to improving the recognition rate and accuracy of attitude. The experimental results show that the algorithm presented in this paper has advantages over other classical identification algorithms in catalog volume and recognition rate, with the recognition rate reaching 99.9%, and catalog volume having the least increase with the increase of the magnitude. The proposed algorithm is more applicable for online application of the star sensors with wide FOV and high magnitude.
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