The high-energy laser weapon is famous for its unique advantage of speed-of-light response which was considered as an ideal weapon against Unmanned Aerial Vehicle(UAV). However, due to the high energy laser reflection effect, the pixel gray distribution of the frame image will be changed drastically, and therefore the miss distance signal will be interfered strongly when the high energy laser irradiating on the UAV, which seriously affects precision of object tracking in practical application. The traditional “centroid method” or “template matching method” have been difficult to meet the requirements of high precision miss distance which was less than 1pixel(RMS) under the reflected light interfering. In order to developing operational effectiveness of weapon system, G-DS(Gray weighted factor-Diamond Search method) algorithm was proposed which combined with gray weighted factor based on self-learning mechanism. It has been studied for the characteristics of UAV images by field experiment. The results show that G-DS algorithm is low-latency(less than 5ms), which can reduce time complexity compared with the traditional ME algorithm, furthermore, G-DS algorithm was robust based on local motion vector of the block, which can improve ability of target detection and recognition compared with the traditional “centroid method” or “template matching method”. Hence, G-DS algorithm was beneficial to the engineering of high-energy laser weapon.