Target trajectory prediction based on neural network and Kalman filtering
Lingxiao Li, Guangli Sun, Jiang-peng Song · 2019
Kalman filtering is a filtering method based on minimum mean square error. It is a filtering algorithm formed by the state equation of the system, the observation equation and the statistical characteristics of the process noise of the system. It is widely used in the field of target tracking navigation guidance, etc. The Kalman filter requires an accurate state model of the known system, so it has great limitations in practical applications. Because Neural Networks have strong nonlinear mapping capabilities. In this paper, a variety of motion models are selected for reference and simulated by Matlab. The simulation results show that the prediction effect of the filter optimized by neural network is better than that of ordinary Kalman filter.