一种鲁棒Sigma-point滤波算法及其在相对导航中的应用

Robust Sigma-point Filtering and Its Application to Relative Navigation

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。

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.