The multidimensional assignment model is to utilize maximum likelihood estimation instead of the true target position for constructing the cost function without direct consideration of random error. In view of this problem, a data association algorithm is proposed based on the Renyi entropy. The algorithm is to utilize Renyi entropy to quantify difference of functions between the probability density function of pseudo measurements and the most posterior probability density function for constructing an association cost. The results show that the proposed algorithm can improve the correct association ratio and achieve a good performance as well.