An improved cubature Kalman filter state prediction method based on the design of active denial tracking system

In this paper, an improved CKF (Cubature Kalman Filter) target tracking method is adopted to solve the tracking and pointing problem in the field of the Active Denial System. The math model of the system is built and the precision requirement is analyzed. The improved CKF method is input as the feedforward compensation for system control to improve the system tracking performance. In the process of the iterative CKF algorithm, nonlinear means are used. The method makes full use of measurement information and estimates the target velocity acceleration model parameters through the neural network, which is used as the input of the CKF to modify the process parameters of CKF and improve the state estimation accuracy. At the same time, the limited lower bound method is used to ensure that the gain reaches the lower bound bottom line of the precision demand, so that it does not tend to zero with time, so as to avoid affecting its rapid response ability during maneuvering and so that the prediction error is also controlled within the range of the precision demand. The simulation and experimental results show the superiority of the method and make the system fully meet the design requirements.