基于无模型无迹粒子滤波的编队卫星相对运动估计

由于地球引力和大气阻力等因素造成的模型不确定性，使常规滤波方法用于卫星编队飞行相对运动估计时精度不高。为克服这种影响，提出了一种融合高斯过程回归(Gaussian process regression，GPR)的无模型无迹粒子滤波(model free unscented particle filter， MF-UPF)方法。对近圆轨道的双星编队问题，新方法通过高斯过程回归对已有的量测数据学习建立相对运动模型, 有效地避免了模型不确定性造成的滤波性能下降。仿真对比验证了无模型无迹粒子滤波在编队飞行相对运动估计中的优越性。

Estimation of formation spacecraft relative motion based on model-free unscented particle filter

Normal filter algorithms cannot achieve high precision due to the modeling uncertainty caused by the earth gravity and atmospherical drag.A model-free unscented particle filter(MF-UPF) combined with Gaussian process regression is presented to overcome modelling uncertainty.Gaussian process is used to establish a relative motion model of formation flying satellites in near-circular orbits using training data,which efficiently avoids degradation of filtering performance.Simulations and comparisons validate the superiority of MF-UPF for the relative motion estimation of formation flying.