Aiming at problem that multi-dimensional assignment model which uses maximum likelihood estimation as true target position has ignored the random errors when constructing the cost function

a data association algorithm is proposed based on information entropy. To improve the completeness of the multi-dimensional assignment model

relative entropy and Renyi entropy are used to quantify the difference between the probability density function of pseudo measurements and the most posterior probability density function to construct the association cost. The simulation results show that the proposed algorithm can improve the correctness and achieve better performance as well.