Optical fiber intrusion signal unmixing by constrained quadratic programming approach

Optical fiber intrusion signal detection is an effective long-distance sensing technology for perimeter intrusion behavior. The existing optical fiber pre-warning system (OFPS) has been able to detect and identify a variety of intrusion behaviors. However, when various intrusion behaviors occur at the same time, identification performance in OFPS will decrease. In this paper, we model the mixed intrusion signal from a geometric perspective at first, and then the unmixing method is studied under the model constraint condition. We set the model error function as an objective function, and transform the unmixing problem into a constrained quadratic programming problem. Through iteration, the pure intrusion signals and their proportion are obtained. This method does not require prior knowledge such as the number of pure components and corresponding characteristics. Compared with the conventional methods, the proposed algorithm can obtain more accurate features under unsupervised conditions.