Disturbance pattern recognition based on an ALSTM in a long‐distance φ‐OTDR sensing system

In this article, a new pattern recognition method for disturbance signals detected by phase‐sensitive optical time domain reflectometry (φ‐OTDR) distributed optical fiber sensing systems is proposed. Currently, most of the disturbance signal recognition methods for φ‐OTDR exploit the global features of disturbance signals as the basis of classification, neglect the local details of disturbance signals, and thus have poor performances on long‐distance monitoring tasks. In the method proposed in this article, an adaptive denoising method based on spectral subtraction is utilized to enhance signal features. For each frame of disturbance signals, Mel‐frequency cepstral coefficients are extracted as frequency‐domain features, while short‐time energy ratio and short‐time level crossing rate are extracted as time‐domain features. An attention‐based long short‐term memory network is exploited as a classifier to recognize different types of disturbance signals. Experiments show the proposed disturbance recognition method can achieve a classification accuracy of 94.3% with five typical disturbances, namely, walking, digging, vehicle‐passing, climbing, and heavy rain, at ranges of up to 50 km.