认知导航路径整合中方位及尺度参数求解方法

无人作战飞机(UCAV)认知导航路径整合时需获取环境信息相对参考信息的方位和尺度值,提出了一种基于迭代最小二乘法的高精度相似变换参数求解方法.利用SURF(Speeded Up Robust Features,SURF)算法提取高鲁棒性特征点,采用比值法提纯匹配对,得到一一对应点集,并将点集中元素转换为矢量形式,以最小二乘算法求解矢量间相似变换参数,并根据结果对感知图进行方向旋转及尺度调整,通过循环迭代得到方位和尺度值.仿真结果表明,文中方法得到的角度平均绝对误差低于0.04°,尺度变化下得到的尺度绝对误差在此10-3数量级,抗噪声性能优于最小二乘法.

Method of Solving Parameters of Orientation and Scale for Cognitive Navigation's Path Integration

In order to achieve relative orientation and scale values between environmental information and reference information for path integration of UCAV cognitive navigation, a method based on iterative least-squares is proposed to solve high-precision similarity transformation parameters. SURF algorithm is used to extract high robustness feature points, then ratio method is taken to purify the matching pairs to get a point-to-point set, the elements in the set are further transformed into vectors, similarity transformation parameters are gained by iterative least-squares algorithm, then the perceived image''s orientation is rotated backward, and the scale is adjusted inversely according to the gained parameters, circulate the operations until relative orientation and scale value are finally obtained. The simulation results show that the use of the above method can get high-precision orientation and scale parameters and anti-noise performance is superior to the least squares algorithm.