Multisource Threatening Event Recognition Scheme Targeting Drone Intrusion in the Fiber Optic DAS System

Although fiber-optic distributed acoustic sensing (DAS) has been widely applied for intrusion recognition in the perimeter security field, challenges still remain for high-accuracy recognition of new types of intrusion events. Drone, as a noncontact, stealthy intrusion event, has not been reported to achieve effective monitoring and recognition by employing the fiber-optic DAS technology. By introducing drone intrusion, the scope of the threatening intrusion in the perimeter security monitoring field is extended. This study proposes a multisource threatening event recognition scheme targeting drone intrusion in the fiber optic DAS system. To achieve this objective, a dual-stage recognition method is proposed. Besides, the wavelet denoising method is applied to extract the effective signal from weak disturbances introduced by drone flight. The variational mode decomposition (VMD)-based hybrid feature vector is formed to remove the noise and further extract the effective signal characteristics. Next, we demonstrate a hybrid model framework based on convolutional neural network (CNN) + long short-term memory (LSTM) + self-attention mechanism to achieve the effective recognition of multisource threatening event targeting drone intrusion. Particularly, we thoroughly discuss the distinguishing ability between drone intrusion and nonthreatening wind blowing and the recognition ability for the simultaneous occurrence of both human-contact intrusion and noncontact drone intrusion. The experimental results show that the proposed recognition scheme can effectively distinguish the drone intrusion from the nonthreatening wind-blowing event with a high accuracy of 100%. In addition, when drone intrusion, environmental disturbances, and human-contact intrusion occur simultaneously, a high recognition accuracy of 96.25% is achieved with a fast response time of 0.733 s.