The corner reflector is a kind of passive interference method which commonly used in ship electronic confrontation, there is no significant difference in the echo signal of the ship in the time domain, frequency domain and airspace. The polarization transformation characteristic of the target is intrinsically related to its shape and structure, and the antijamming effect of the target can be solved by using the polarization characteristic. In this paper, polarized scattering data are obtained by simulating ship and corner reflector models with CST software, the polarized invariant and the target polarized shape factor are calculated by polarized scattering data. SVM is used as a classifier to classify the ship and corner reflector based on the polarization invariant and the target polarization shape factor. The result shows that the method it is possible to effectively classify the ship and the corner reflector
Fault prediction is an important component of health management, and plays an important role in the reliability guarantee of complex electronic equipments. Transmitter is a unit with high failure rate. The cathode performance of TWT is a common fault of transmitter. In this dissertation, a model based on a set of key parameters of TWT is proposed. By choosing proper parameters and applying adaptive neural network training model, this method, combined with analytic hierarchy process (AHP), has a certain reference value for the overall health judgment of TWT transmitters.