MFR Working Mode Recognition Based on CNN-BILSTM-SoftAttention Model

Abstract

Accurate identification of MFR working mode recognition is an essential prerequisite for target threat assessment. To solve the problem of lower recognition rate of radar pulse signals with overlapping parameters, a hybrid recognition model based on CNN-BILSTM-SoftAttention is proposed. Firstly, We utilize the combined CPI parameters to describe pluse stream and capture local characteristics with CNN. Then, the BILSTM Network is used to analyze the timing regularity of radar pulse sequences, and to discover the inter-class rule between different working modes and the intra-class rule of the same working mode. Finally, combined with the attention mechanism model, we can distinguish different working mode by assigning higher weights to parameters with overlapping. Through simulation analysis, the proposed algorithm is compared with SVM, CNN, CNN_LSTM method, the accuracy of model can reach 92.48% in the strong noise environment, increasing by 20%. The results show that the proposed method has better classification ability and higher performance than existing work pattern classification methods.