一种无人机视觉导航方法及其滤波算法改进

设计了一种无人机视觉/惯性组合导航系统,将无人机和地标点的运动模型作为状态方程,视觉信息作为观测量构建了与之对应的滤波模型.在滤波处理上,采用了复杂加性噪声模型对系统噪声进行建模处理;将小波分析引入到UKF(Unscented Kalman Filter)滤波中得到小波-UKF滤波算法,以此克服视觉观测噪声对滤波的影响;采用最大后验概率准则(MAP,Maximum A Posterior)自适应估计观测噪声协方差阵,并将其反馈到滤波过程中克服了小波处理后观测噪声方差阵不易确定的不足.仿真结果证明:对滤波算法的改进可以有效地提高滤波估计的精度.

Vision based navigation system of UAV and improvements of the corresponding filtering algorithm

A vision/inertial integrated navigation system was built. The corresponding filtering model was established by treating the motion models of aerial vehicle and landmark as the system function and the vision information as the observation. Complex additive noise model was adopted to describe the system noise in the filtering process. The wavelet-unscented Kalman filter (UKF) algorithm was obtained by introducing the wavelet analysis into UKF, thus the influence of vision observation noise on the filtering was inhibited successfully. Maximum a posterior (MAP) adaptive method was utilized to estimate the observation noise covariance matrix, which was further fed back into UKF to overcome the difficulties in identifying the covariance of observation after the wavelet de-noising. The simulation proved that the improvements in the filtering process to be effective in increasing the filtering accuracy.