Fractional lower order time-frequency spectrum and transfer learning method for underwater JANUS signal recognition

Non-cooperative and third party underwater standard protocol signal recognition has important research significance in the field of underwater acoustic communication signal recognition.Feature extraction of shallow sea JANUS signal is easily affected by complex underwater acoustic interferences such as impulsive noise and multipath effect,which lead to low recognition rate.To solve this problem,a recognition method based on fractional lower order time-frequency spectrum and Residual Network 18 is proposed.First,the JANUS preamble signal is selected as the recognition object,a fractional lower order fourier synchrosqueezing transform method is designed to suppress impulsive noise by operation of fractional lower order and to improve time-frequency concentration by characteristics of timefrequency rearrangement.Secondly,ResNet18 pre-training model based on ImageNet is fine-tuned,then we train the time-frequency image sets of JANUS signal and other common underwater acoustic signals on this network,and the time-frequency domain features are extracted for recognition.The results of the simulation shows that the proposed algorithm has a high recognition rate of 96.15% when the SNR is-10 dB,it can suppress impulsive noise and reduce the influence of multipath effect,and it has better recognition performance than traditional algorithms.In the sea test,the recognition rate of JANUS signal is 90.00%,confirming that the recognition accuracy of the algorithm and the generalization of the network are relatively high.