A new star pattern recognition method is developed using singular value decomposition of a measured unit column vector matrix in a measurement frame and the corresponding cataloged vector matrix in a reference frame. It is shown that singular values and right singular vectors are invariant with respect to coordinate transformation and robust under uncertainty. One advantage of singular value comparison is that a pairing process for individual measured and cataloged stars is not necessary, and the attitude estimation and pattern recognition process are not separated. An associated method for mission catalog design is introduced and simulation results are presented.

中文翻译：

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一种有效且鲁棒的奇异值方法，用于星图识别和姿态确定

开发了一种新的星形模式识别方法，该方法利用测量框中的被测单位列矢量矩阵和参考框中相应的分类矢量矩阵的奇异值分解。结果表明，奇异值和右奇异矢量对于坐标变换是不变的，并且在不确定性下具有鲁棒性。奇异值比较的一个优点是，不需要对单个实测星和分类星进行配对，并且不会分离姿态估计和模式识别过程。介绍了一种相关的任务目录设计方法，并给出了仿真结果。