Open-set Recognition of Radar PRI Modulation Type Based on Improved EVM
The modulation type of the radar pulse repetition interval(PRI)is an important means to analyze the working state and task of the radar.In order to solve the problem that common radar PRI modulation type recognition algorithms cannot identify un-known modulation types,an open-set recognition method for radar PRI modulation types based on improved extreme value machine(EVM)is proposed.Firstly,the residual network and bi-directional long short-term memory network is used to extract the features of PRI sequence.Secondly,combined with prototype learning,the feature extraction network is trained by distance-based cross-entropy loss and prototype loss.Finally,an improved EVM model is proposed by introducing a linear combination of known class features into the feature space to simulate the behavior of unknown classes.Experimental results show that compared with EVM,the proposed method can improve the recognition accuracy of radar PRI modulation type,and has good open-set adaptability in open electromagnetic environment.
pulse repetition interval(PRI)modulation typeopen-set recognitionresidual networkbi-directional long short-term memory(BiLSTM)prototype learningextreme value theory
万方数据
维普
