Data Prediction Compensation For Dynamic Target Tracking System Based On Bp Neural Network

During the target tracking process, some observation data may be missing due to the equipment problems or the operation errors, which may affect the filtering process of the target state and the position determination accuracy. Therefore, the missing data needs to be effectively compensated. This paper provides a method to compensate the missing data by using the characteristics of BP neural network learning system aiming at the dynamic target tracking system. The neural network is trained by using the complete data of the dynamic system, and then the missing data is predicted by the trained neural network. The simulation results for both of the linear system and the nonlinear system show that the method is indeed effective, compared to the traditional time update prediction, the prediction accuracy is much higher.