基于概率马尔可夫模型的鲁棒接近操作
Robust Proximity Operations using Probabilistic Markov Models
AI总结：
本文提出基于马尔可夫决策过程的状态切换框架，结合扩展卡尔曼滤波位姿估计与多状态制导，实现小卫星对接和飞行器精确着陆的鲁棒接近操作。
摘要：
针对多种自主飞行器的接近操作，设计、实现并分析了一种基于马尔可夫决策过程的状态切换方法。该框架包含一个位姿估计器以及一个多状态制导算法。统一的位姿估计器利用扩展卡尔曼滤波器融合来自速率陀螺仪、单目视觉和超宽带雷达传感器的测量数据，并配备基于马氏距离的异常值剔除和测量值降权机制，以实现鲁棒性能。提出使用概率马尔可夫模型在不同制导模式之间进行切换，以实现鲁棒且高效的接近操作。最后，通过对两颗小卫星对接和一架飞行器精确着陆的实验分析，验证了该框架的有效性。
英文摘要：
A Markov decision process-based state switching is devised, implemented, and analyzed for proximity operations of various autonomous vehicles. The framework contains a pose estimator along with a multi-state guidance algorithm. The unified pose estimator leverages the extended Kalman filter for the fusion of measurements from rate gyroscopes, monocular vision, and ultra-wideband radar sensors. It is also equipped with Mahalonobis distance-based outlier rejection and under-weighting of measurements for robust performance. The use of probabilistic Markov models to transition between various guidance modes is proposed to enable robust and efficient proximity operations. Finally, the framework is validated through an experimental analysis of the docking of two small satellites and the precision landing of an aerial vehicle.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
