基于高斯过程回归的光度数据姿态估计
Attitude Estimation from Photometric Data using Gaussian Process Regression
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中文总结 AI 辅助
针对空间目标参数未知的问题，提出结合高斯过程回归与无迹卡尔曼滤波的姿态估计方法，通过光变曲线实现高精度、鲁棒的姿态估计，适用于空间态势感知场景。
中文摘要 AI 辅助
地球轨道上的空间目标数量快速增长，对先进的空间态势感知与空间域感知需求日益迫切，以管理卫星交通并防止碰撞。姿态估计对精确的状态传播至关重要，因为太阳辐射压力、大气阻力等非引力力取决于目标的姿态。本研究探索利用光变曲线（即目标亮度随时间的变化）估计空间目标的姿态。光变曲线反演传统用于天文学，应用于空间目标时面临挑战，因其具有非凸形状和镜面反射。传统姿态估计方法通常假设形状和表面参数已知，但碰撞或解体产生的空间碎片的这些参数通常未知。为解决此问题，本研究提出结合高斯过程回归（Gaussian process regression）与无迹卡尔曼滤波（unscented Kalman filter）的估计方法，利用高斯过程回归构建非参数观测模型，增强对未知表面参数的鲁棒性。数值示例考虑地球同步轨道上的箱形翼状目标，结果表明，所提方法的估计精度优于传统无迹卡尔曼滤波；数值模拟结果还显示，该姿态估计对表面特性的不确定性具有鲁棒性，适用于目标参数未知的空间态势感知与空间域感知实际场景。
英文摘要
The rapid growth of resident space objects in Earth's orbit has intensified the need for advanced space situational awareness and space domain awareness to manage satellite traffic and prevent collisions. Attitude estimation is critical for accurate state propagation, as non-gravitational forces like solar radiation pressure and atmospheric drag depend on the object's attitude. This study explores using light curves, time variation of an object's brightness, to estimate a space object's attitude. Light curve inversion, traditionally used in astronomy, faces challenges when applied to resident space objects due to their non-convex shapes and specular reflections. Conventional methods for attitude estimation often assume known shape and surface parameters, which are usually unknown for space debris generated by a collision or breakup. To address this issue, this study proposes the estimation method combining Gaussian process regression with the unscented Kalman filter. This study uses Gaussian process regression for a non-parametric observation model, enhancing robustness against unknown surface parameters. Numerical examples consider a box-wing object in a geosynchronous orbit and demonstrate that the proposed method has better estimation accuracy than a conventional unscented Kalman filter. The numerical simulation results also represent the attitude estimation robust against uncertainties in surface properties, contributing to practical scenarios in space situational awareness and space domain awareness where the object parameters are unknown.
