Abstract: The traditional radar target tracking methods only utilize the information of target position to finish data association. When these methods are used to deal with the problem of multi-target tracking in the dense clutter, it is easy to generate the false tracks or even to lose tracks. Aiming at this problem, a multi-target tracking algorithm aided by Doppler information is proposed in this paper. The problems of the nonlinear measurement and the correlation relationship between range and Doppler measurements are considered in the proposed algorithm. Firstly, the multi-dimension correlating gate is constructed with the information of target position and velocity based on the frame of integrated probabilistic data association and unscented Kalman filter (IPDA-UKF). The data association is accomplished with the multi-dimension information. So the problem of multi-target data association is simplified to multiple sub-problems consisting of a single target data association. Secondly, the existing probability and motion state of each target are estimated by the IPDA-UKF algorithm respectively. The simulation results and comparison with the other algorithms reveal that the proposed algorithm has reduced the computing complexity of multi-target data association, and improved the efficiency of data association by using the correlation between range and Doppler measurement completely on the one hand. On the other hand, the tracking accuracy is also improved by the aid of Doppler information.