Radar Emitter Type Identification Effect Based On Different Structural Deep Feedforward Networks

A radar emitter identification method based on depth network model is studied in this paper. Based on analysing the matching correlation of radar emitter data and the characteristics of deep network model, the estimated parameters sample diagram (EPSD) is used as the input form of parameter data, and the deep feedforward network (DFN) is used to fit the abstract function of mapping input data to types. After further detailed analysis of the network model, the conclusion of network parameter setting is given, and the fine adjustment of network is realized. The simulation results show that the identification rate of radar emitter can be greatly improved by using the deep network model. It is obviously there is information existing in the characteristics of radar emitter that can be mined by deep feedforward network.