基于自适应窗口选择PIV技术的序列星图运动估计方法

对星空观测CCD相机获得的序列图象，由于相机姿态的变化或者观测平台位置的改变，观测的序列图象存在全局运动，需要对星图的运动参数进行准确的估计。粒子图象测速技术（Particle Image Velocimetry, PIV）的发明和发展标志着现代实验流体测量技术的重大突破。PIV技术在流体力学及空气动力学研究领域具有较高的学术意义和实用价值，本文提出一种基于自适应窗口选择PIV技术的序列星图运动参数估计算法。算法通过自适应选取窗口使得分析窗口内的恒星成像密度达到最大，且窗口边缘区域不存在高亮恒星成像，然后对分析窗口进行互相关计算，求得序列星图的运动参数。结果证明本文算法在很大程度上提高了互相关运动参数估计的精度，将运动参数估计误差降低到0.01pixel以内。

A Motion Estimation Algorithm of Serial Star Images based on Self-adaptive Window Selection PIV technique

For the star observation system, because of the movement of CCD flat, the CCD star serial images exist global movement. So, it is needed to estimate the motion parameter precisely. The invention and development of particle image velocimetry(PIV) indicate the important breakthrough of modern experimental fluid measurement. PIV is important both in the theoretical studies and application of the fields of fluid mechanics and aerodynamics. A motion estimation algorithm of serial Star images based on self-adaptive window selection PIV technique is proposed in this paper. The algorithm chooses analysis window of maximum energy by a self-adaptive method. Then the global motion parameter can be estimated through cross-correlation of the two analysis window. The experimental results demonstrate the algorithm is of high accuracy, and reduce the estimation error to be less than 0.01 pixel.