Accurately selecting the fitting function of trajectory is the key step to calculate the target-missing parameters properly in targetmissing Quantity processing. In this paper, we firstly utilize the regression model with hypothesis testing, choose the reasonable fitting model and confirm the ordernumber of the function. It is found that this method improves the function approximation and decreases the error of fitting function during choosing the model based on our experience. Secondly, we calculate the parameters of targetmissing using dualtrajectory data fusion algorithm. This method eliminates the jump phenomenon of the connections between the trajectory parameters. The results of realdata processing indicate that the algorithm proposed in this paper performs better than the traditional methods, which validate the feasibility and superiority of this approach.