Detection of Weak Pulse Signal in Chaotic Noise - Based on Att-CNN-LSTM model

Abstract

A hybrid neural network based on the attention mechanism was proposed to achieve the detection of weak pulse signals in chaotic noise. Firstly, based on the high sensitivity to small interference and short-term predictability of chaotic signals , the phase space of observed signals was reconstruction. Then, Att-CNN-LSTM, a hybrid neural network based on the attention mechanism was proposed to predict chaotic signals, and the one-step prediction error was obtained, The detection problem of the observed signal can be transformed into a signal detection problem for the one-step prediction error. Finally, the weak impulse signal was detected from the prediction error by using the Z-test method. In the simulation experiments, the results of the proposed model were compared with those of the single convolutional neural network (CNN) and long short-term memory neural network (LSTM) model, the least square support vector machine, and the CNN-LSTM model without the attention mechanism. The simulation results show that the proposed model has higher prediction accuracy than other models at different signal-to-noise ratios(SNR), and achieves good performance in the detection of weak pulse signals when the SNR is greater than -140.91dB.