The inaccurate positioning of dynamic stars is the main challenge faced by dynamic star sensors. To reduce the star positioning error, researchers have focused on refining detection and positioning methods, but such methods still employ conventional exposure for star images. This study introduces a novel approach to reducing star positioning errors by using coded exposure. In this study, two key models for the encoded star strip are established: 1) the energy distribution model, based on the coded line-spread function (CLSF), and 2) the positioning error model, described by coded length factors. Based on the models, we demonstrate the principle of star positioning accuracy improvement by using coded exposure, and we derive the optimal code for minimizing positioning errors. The experimental results validate the correctness of the proposed models and show that using identical detection and positioning methods, compared with conventional exposure, the proposed coded exposure approach can decrease star positioning errors by more than 35% under the condition of 5°/s.

中文翻译：

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星敏感器编码曝光提高动态星定位精度的方法

动星定位不准确是动星敏感器面临的主要挑战。为了减少星体定位误差，研究人员一直致力于改进检测和定位方法，但这些方法仍然采用传统的星图曝光方式。这项研究介绍了一种通过使用编码曝光来减少恒星定位误差的新颖方法。在本研究中，建立了编码星带的两个关键模型：1）基于编码线扩展函数（CLSF）的能量分布模型，2）通过编码长度因子描述的定位误差模型。基于该模型，我们论证了利用编码曝光提高星体定位精度的原理，并推导出最小化定位误差的最佳代码。实验结果验证了所提模型的正确性，表明采用相同的检测和定位方法，与传统曝光相比，所提出的编码曝光方法在5°/s条件下可以将星体定位误差降低35%以上。