In order to solve the problem of tracking failure caused by complex scenarios such as fast motion, occlusion and scale variation, a high-speed correlation filtering target tracking algorithm integrating motion state information is proposed. This paper makes three improvements based on the traditional Discriminative Correlation Filter: (1) The Kalman filter is added to the tracking process to modify the predicted position by using the motion state information, so as to deal with the tracking failure caused by fast motion and improve the tracking accuracy; (2) A separate filter for scale estimation is learned and the PCA method for dimension reduction of features is used to improve the tracking speed. (3) A high-confidence update strategy is proposed to determine whether the position filter is updated and whether the predicted position is transferred to Kalman filter for correction. The algorithm is tested on OTB-100 platform with several state-of-the-art tracking algorithms. Experiments show that our algorithm's average precision and success rate can reach 74.8% and 69.8%, respectively, and the average speed is 84.37 frames per second. Compared with other algorithms, the proposed algorithm can effectively improve the tracking performance, guarantee the tracking speed, and keep good tracking effect under complex conditions such as occlusion, ambiguous background and fast motion.