摘要:在立体视觉测量中,为获得更高精度,往往需要将视差计算精确到亚像素级。将互信息理论引入双目图像配准并结合多分辨率技术实现亚像素级点匹配。采用Bouguet 立体校正算法对左右图像进行极线校正,利用Harris角点探测器检测目标并将获取角点作为待匹配点,采用最大互相关法进行搜索确定像素级匹配点。然后对以左右匹配点为中心的20×20邻域图像进行插值并分别放大10倍和100倍,采用互信息方法先对低分辨率图像进行配准,再在高分辨率图像上进一步细化求精,结合像素级匹配的整数视差可得最终亚像素级视差。实验结果表明,该方法能将视差精度提高到0.01像素。%In order to improve the range-measuring accuracy in stereo vision,a sub-pixel parallax is needed.The Mutual Information (MI) theory is combined with multi-resolution method to realize the goal of sub-pixel point matching .Firstly,Bouguet algorithm is used to rectify the left and right images making their epipolar line forward-paralled .Then Harris corner detector is brought to find a most characteristic corner as candidate matching point .After that,Most Cross Correlation matching rule is introduced to search the matching point .The 20 ×20 areas whose center is the left and right matching point are magnified by 10 and 100 times respectively .The low-resolution image is registered with MI theory,followed by the seeking of higher precision in the high-resolution image .Finally,combined with the integer-grade parallax,we can get the sub-pixel parallax .The experimental result shows that the method used in this article can improve the precision to 0.01 pixel level.
摘要:多无人机超视距空战决策问题是现代空战重要的研究课题,针对多无人机超视距空战博弈问题进行了研究。首先根据敌我双方作战态势参数信息,建立敌我双方对抗支付博弈模型,然后给出了基于量子粒子群算法的空战博弈混合策略纳什均衡的求解方法,最后通过仿真验证了该方法的可行性及有效性,为解决在超视距下多无人机空战策略问题提供了一种较科学的方法。%Decision-making in multi-UAV beyond-visual-range air combat is an important research topic of modern air combat .According to the air combat situation and combat information,the revenue function of UAV air combat game and game payoff matrix under the information were established .Then,a mixed strategy Nash equilibrium method for air combat game was established based on Quantum Particle Swam Optimization ( QPSO) .The simulation result validates the feasibility and effectiveness of the proposed method,which supplies a scientific method for solving the problem of decision-making in beyond-visual-range air combat of UAVs .