为了解决在目标跟踪系统中，传统相关算法在目标发生目标局部遮挡或旋转等姿态变化较大的情况时容易跟踪丢失的问题,提出一种改进的基于卡尔曼预测器的环形模板匹配相关跟踪的算法.利用卡尔曼预测器来预测下一帧目标可能出现的区域，然后在较小的预测区域中进行环形相关匹配运算，找到最佳相关匹配点，使跟踪更具主动性。环形匹配还可以克服由于姿态变化而引起的横向匹配点丢失，从而可以跟踪各种姿态运动的机动目标.实验中,利用改进算法对出现局部遮挡情况的姿态变化大的运动目标进行跟踪，传统算法处理此类情况容易跑飞，而本文算法不受这两种跟踪局限性的干扰，始终稳定跟踪机动目标且耗时大幅减少.

The global searching method is adopted in the traditional correlation algorithms in target tracking system, resulting in the large amounts of calculation, the difficulty of real-time implementation and even the loss of the tracking target when the target is partially shaded or in sharp attitude variation. To solve the problems, an improved approach based on the loop correlation template matching and Kalman filter is proposed to realize target tracking. Kalman filter, to make the tracking more active, estimates the possible region of the target in the next frame at first,and finds the best matching points by the loop correlation matching in the aforementioned region while the loop-template matching overcomes the loss of the horizontal matching points caused by the sharp attitude variation. So the algorithm has strong robustness, and is of translation and rotation invariance. In the experiments, the proposed algorithm is applied to the tracking of a main battle tank partially shaded and a jet with “Viktor Pugachev′s Cobra”. Compared with the traditional algorithms, the algorithm herein can steadily track the targets in less time without losing them.