msCNN-LSTM perimeter intrusion vibration signal identification method based on ultra-weak FBG arrays

•In this paper, a new feature extraction and recognition method for perimeter intrusion vibration signals is proposed, using msCNN to extract multi-scale structural features of the signal, and combining features of different scales at the end of each convolutional block.•Before entering into the next convolutional block, the attention mechanism is used to recalibrate the combined features to enhance the expression ability of the features.•Finally, the time dependence is analyzed by LSTM, so that the characteristics of the signal are richer and more comprehensive, so as to obtain a higher recognition rate. To improve the recognition rate of ultra-weak fiber Bragg grating (UWFBG) arrays in perimeter security monitoring, this paper proposes a msCNN-LSTM combinatorial model recognition method, which combines an improved multi-scale convolutional neural network (msCNN) and a long short-term memory neural network (LSTM) to identify intrusion behaviors more accurately by synchronously extracting the multi-scale structural features and time-dependent relationships of vibration signals. Using msCNN to cross-learn multi-layer features, the hidden information between features at different scales can be obtained. The attention mechanism was applied to recalibrate the combined features extracted from the shallower layers, which enhanced the expression ability of features. Finally, the time dependence is analyzed by LSTM. Experimental results show that the scheme can effectively distinguish five typical intrusion behaviors, and the average recognition rate can reach 97.84%.