本文提出了一种基于位置预测的动态规划方法, 用于快速检测GEO带天基观测图像中的暗弱碎片。首先对实测数据进行分析, 得出不同GEO碎片在图像中的运动速度相似, 接着, 进一步对数据做拟合, 得到了GEO碎片速度和观测卫星星下点纬度之间的映射模型, 并提出了GEO带天基观测图像中碎片速度估计方法。最后, 利用估计速度预测碎片位置, 在传统动态规划递归方程中采取位置信息加权, 得到了基于位置预测的目标搜索范围, 从而减少了递归方程中的目标状态个数。实测数据验证了速度映射模型的拟合偏差在1 pixel以内。选取一个典型观测周期的实测数据进行实验, 结果表明本文方法的检测时间比传统动态规划方法减少了90%以上, 虚警率降低了5.9%以上, 适合于GEO带天基观测图像中暗弱碎片的检测。

An improved dynamic programming method based on position predication was proposed in the paper to quickly detect dim and small debris from the space-based observed images on GEO band. Firstly, after analyzing the observed data, we found that different GEO debris have same movement velocity in images, after further data fitting, we obtained a mapping model between the velocity of image debrits and sub-star latitude of observation satellite and proposed a method to estimate the debris velocity in space-based observation images on the GEO band, at last, debris position was predicted by estimating velocity,then position information weight was applied in traditional dynamic programming recursive equation, and the object searching range was obtained based on position prediction,resulting in the number of object status in the recursive equation were reduced. Measured data verified that fitting deviation of velocity mapping model was within 1pixel. We selected the measured data within typical observation period for test and the results show that the detection time taken by proposed method has been reduced by more than 90% as compared with traditional dynamic programming methods, and the virtual scenery rate has also been reduced by more than 5.9%.It can be conclusion that the method is suitable for detection of dim and small debris from the space-based observation images on GEO band.