Fiber distributed acoustic sensing using convolutional long short-term memory network: a field test on high-speed railway intrusion detection

This paper presents a novel and general distributed acoustic sensing (DAS) signal recognition framework aimed at real-time detection and classification of intrusion in the space-time domain. The framework is based on the combination of a convolution neural network (CNN) and a long short-term memory network (LSTM). The convolutional structure extracts the spatial features from multi-channel signals of the DAS system, while the LSTM network analyzes the temporal relationships over time. The framework can be deployed on high-speed railways for real-time intrusion threat detection, which is one of the most urgent and challenging problems that needs to be resolved as there is an increasing demand for high detection and low false alarm rates, and short response time. The alarm sensitivity and specificity of the framework are controlled by user-set parameters. A real field experiment is conducted in a strong background noise scenario and an intrusion threat detection rate of 85.6%, with only 8.0% false alarm rate is achieved. For threat classification, the average threat detection rate is 69.3%, and the average false alarm rate is 13.2%. Owing to the high detection accuracy of the framework, the average detection response time is shortened to 8.25 s.