基于改进UKF的加油机位姿预测方法

针对无人机自主空中加油保持阶段加油机位姿跟踪精度不高的问题，提出了一种改进UKF（无损卡尔曼滤波）预测方法。建立了视觉导航系统模型，利用Harris算法检测角点，并用RANSAC（随机序列一致性）算法进行角点匹配。将历史预测数据引入当前时刻UKF预测值，并通过匹配角点所得姿态观测值对改进UKF预测值进行修正，从而实现加油机姿态的高精度预测。仿真结果表明，改进UKF在遭遇突发强干扰时姿态预测性能明显优于标准UKF，所预测误差小于5.8%，满足空中加油精度要求。该算法避免了强干扰引发的预测出错，有效抑制了突发干扰。

Tanker pose prediction based on modified unscented Kalman filter

Aiming at the low accuracy problem of the tanker pose tracking in the autonomous aerial refueling, a modified unscented Kalman filter(UKF) algorithm was put forward. The mathematical model of vision navigation was established. The Harris method was applied for corner detection, and then the random sample consensus( RANSAC) was used to match the detected corner. The historical forecast data was introduced into the current prediction so that the UKF prediction results could be modified with respect to the observation results from the corner match. As a result, the goal of high accuracy prediction was achieved. Compared with the standard UKF, the experimental results show that the prediction error of the proposed method is smaller than 5.8% which is feasible and effective in aerial refueling. The algorithm can eliminate the prediction error caused by the strong inference so as to effectively suppress the strong interference.