月面着陆器INS/CNS深组合导航方法

针对月面着陆器下降过程中的姿态快速调整需求，导航系统必须具备实时快速解算能力，而传统天文导航算法星图识别过程计算量大，占用了一定的硬件资源和能耗，限制了月面着陆器动态响应能力的提高。针对这一问题提出一种基于图像灰度误差的惯性/天文深度组合导航方法。该方法利用惯导辅助星敏感器构建灰度误差函数，根据灰度误差梯度优化姿态失准角，无需星图识别过程依旧可以完成对姿态的估计。仿真结果表明，在导航星数目不少于3颗时，该方法可在与惯性/天文松、紧组合姿态精度一致的前提下，将计算时间缩短60%，姿态精度维持在10″(非光轴)、50″(光轴)以内；在导航星数目小于3颗时，依旧可以进行导航解算，姿态精度维持在50″(非光轴)、100″(光轴)以内。将其应用于月面着陆器等实时性需求较高的背景中，有助于降低算法的计算量，节约硬件资源和系统功耗，为未来月面着陆器导航系统的设计提供新思路和理论参考。

INS/CNS Deeply Integrated Navigation of Lunar Lander

The attitude of a lunar lander needs to be adjusted quickly during its descending. However, the traditional celestial navigation algorithm takes a long time to identify the star and occupy some hardware resources, which wastes power consumption and limits the dynamic response ability of the lunar lander. To solve this problem，a new method based on image gray error is proposed. This method uses the INS data to build the image gray error function and optimizes the attitude along the gradient of image gray error, which can estimate the attitude without star identification. The simulation results show that, when the number of navigation stars is no less than 3, the INS/CNS deeply integrated navigation algorithm can reduce the time by 60%, and the attitude accuracy is maintained within 10″ (non optical axis) and 50″ (optical axis). This method can still work when the number of navigation stars is less than 3, and the attitude accuracy is maintained within 50″ (non optical axis) and 100″ (optical axis). The INS/CNS deeply integrated navigation algorithm helps to reduce the calculation, and save hardware resources and power consumption, which can provide theoretical reference and new ideas for the future design of a lunar lander navigation system.