一种鲁棒Sigma-point滤波算法及其在相对导航中的应用

研究了一种鲁棒Sigma-point滤波方法在无人机编队相对导航问题上的应用。该方法采用Huber估计方法,将Sigma-point滤波量测更新转化为求解线性回归问题,新的Sigma-point滤波方法是一种混合L1、L2范数最小估计,当量测噪声为受污染的高斯白噪声时,该方法具有一定的鲁棒性。给出了编队无人机相对惯导方程和相对视线矢量测量原理,应用鲁棒Sigma-point滤波方法融合相对惯导信息和相对视线矢量信息,估计出无人机之间的相对姿态、相对速度和相对位置。仿真结果表明,与扩展卡尔曼滤波和常规Sigma-point滤波相比,鲁棒Sigma-point滤波可以获得更高的估计精度。

Robust Sigma-point Filtering and Its Application to Relative Navigation

The robust Sigma-point filtering is investigated and applied to relative navigation for unmanned aerial vehicles (UAVs) in formation flight. Making use of Huber-based estimation method, the measurement update of Sigma-point filtering is achieved by solving the linear regression problem. The new Sigma-point filtering which is a combined minimum L₁ and L₂ norm estimator exhibits robustness with respect to deviations from the commonly assumed Gaussian error probability. The relative inertial equation and line of sight measurement are provided. The robust Sigma-point filtering is used to fuse relative inertial information and line of sight mea-surements, estimating the relative attitude, velocity and position. Simulation results show robust Sigma-point filtering is able to achieve more accuracy estimation than standard Sigma-point filtering and standard extended Kalman filtering.