In the target tracking system in complex environment,due to the influence of random pulse interference,modeling error,unknown outliers and other factors,the process noise and measurement noise of the system model show complex non-Gaussian heavy-tailed characteristics.A method based on the KL Divergence (KLD) minimization in the distributed fusion framework is proposed.Firstly,a priori model including many parameters such as target state,process noise and measurement noise is constructed as a studentt t distribution.Secondly,KLD minimization solves the problem of distance in fitting approximate distribution to the real distribution,and improving the accuracy of studentt t modeling.Finally,the Covariance Intersection (CI) fusion strategy is adopted to realize the fusion and correction of local platform state estimation.The simulation results show that the proposed algorithm has higher estimation accuracy compared with the traditional NKF,STF and MCCKF algorithms.