基于自适应SRUKF的无人机位姿预测方法

针对无人机自主导航的实时性差、精度低且对时变噪声的鲁棒性弱的问题,建立了机器视觉和惯性导航相融合的组合导航系统,并提出了一种自适应平方根无迹卡尔曼滤波(adaptivesquare-root unscented kalman filter,ASRUKF)算法。该算法通过观测值与估计值残差的Ma-halanobis距离时刻修正系统噪声协方差,再与采用最小偏度采样的SRUKF算法相融合,从而达到时变噪声自适应抑制,滤波快速且对噪声鲁棒性高的效果。仿真结果表明,相比标准SRUKF,ASRUKF计算耗时减少约38.8%,位移、速度和姿态角预测精度分别提高超过4倍和6倍,且对于时变噪声鲁棒性更强。

Adaptive square-root unscented Kalman filter for position and pose prediction of UAV

To improve the real-time performance, the accuracy and the robustness of the unmanned aerial vehicle (UAV) autonomous navigation system under time-varying noise circumstance, an integrated navigation system of machine vision and inertial guidance was established, and an adaptive square-root unscented Kalamn filter (ASRUKF) was proposed. By introducing the Mahalanobis distance of the residual between the observed and predicted values to the minimal skew sampling square-root Kalman filter, the new algorithm can restrain the system noise adaptively, compute faster and be more robust to noise. The simulation results show that compared with the SRUKF, the ASRUKF is more robust to noise, and the computation time is reduced by about 38.8%, the forecast accuracy of displacement, velocity and attitude angle increases by more than 4 times, 4times, 6 times, respectively.