Abstract. The concept of moving target detection is monitoring and detecting the moving target in the target area, achieving the goal of monitoring the target area all-day and all-weather. During the process of monitoring, the multi-frame SAR images which get from the same scenario need to be registered. The Range Doppler equations are used to obtain the location model of SAR images in this paper, combing the SIFT algorithm with the prior information. Firstly, the SIFT is used to extract the feature points. Secondly, the prior information is used to achieve the regional division of the feature points. Finally, the nearest neighbor matching rule is used to match the targets in the candidate target region. The prior information of SAR images is fully used in this method. In this way, the matching search range is reduced and the efficiency of matching algorithm is improved. Real data simulations prove that this algorithm is feasible. 1.IntroductionSAR imaging technique is used for imaging stationary targets, and GMTI technique is used for detecting and tracking moving targets. With these two technologies, SAR-GMTI system cannot only image for regional targets, but also detect moving target on the ground and form the target track. The realization of SAR-GMIT requires Sequence registration of multiple frames of SAR images in the same scene [1]. Currently, SAR image registration [2] is divided into two methods: gray information-based and features-based. Registration based on features extracts the image feature information to describe the characteristics. Then finds match feature using matching criteria and search methods to complete image registration [3].Lowe proposed SIFT feature point matching algorithm [4,5] Registration method based on the point feature is simple and convenient. However, speckle noise in SAR image can lead to a lot of feature points extracted and it will increase the amount of arithmetic operations. How to reduce the computation and improve the robustness of the algorithm is a key research direction of registration algorithm based on the feature point. This article combines SIFT algorithm with the prior information. It divides the feature points into different region using the prior information to avoid the global feature matching. As a result, the SAR image registration algorithm efficiency is improved. 2.SIFT algorithmThrough a Gaussian differential function, the SIFT algorithm generates a scale space, and selects local extreme point as the candidate feature point, then removes the unstable low contrast and edge response points, and precisely positions feature points [13] [14]. The flowchart of SIFT algorithm for image registrations shown in Fig 1.