To estimate the state estimation about the considered target, and devise the corresponding sensor management, this paper uses Bayesian probability knowledge and maximum likelihood optimum method to estimate the target state estimation. This maximum likelihood method can not only solve the explicit solution of the target state with optimum strategy, but also deal with many constraints about the measurement and state variables. With the closed connection between the error covariance matrix and its Fisher information matrix, we derive the differentiation of the likelihood function with respect to each variable. Using some knowledge from stochastic discrete time system, we give every element which lies in the diagonal line of the diagonalized Fisher information matrix. The trace operation of the Fisher information matrix is applied to be the optimal cost function in the sensor management and then one 0-1 mixed integer numerical programming is used to obtain the sensor distribution matrix. Finally, we apply the sensor management under maximum likelihood target state estimation strategy into the flight control system of UAV in order to confirm the efficiency of the proposed strategy.