Studying on state prediction for radar transmitter using GM(1,1)

Through analyzing the importance of component modules of a kind of radar transmitter, both the cathode current of multiple-beam klystron and the ripple voltage of high-voltage power supply are selected as the prediction signals. By studying on their characteristics, a improved GM (1,1) is proposed to predict the state of transmitter, where the idea of metabolism is applied to make the parameters of GM (1,1) on-line change. Particle Swarm Optimization(PSO) is used to select the best prediction dimension. The performance of the proposed method is tested, and the experiment results show that the proposed method has better prediction capability than the popularly used ARMA model, and then it provides technical support for preventive maintenance.