Optical fiber intrusion signal unmixing by nonorthogonal principal skewness analysis

Abstract

The optical fiber intrusion signal detection technology adopts the distributed optical fiber as sensor to monitor and identify perimeter intrusion signals. Due to the diversity of perimeter intrusion, the optical fiber intrusion signals are composed of various types of pure signals. Therefore, direct identification on mixed signals will cause the system performance degradation. A more effective method is to first unmix the mixed signal to obtain each pure signal component, and then perform signal identification. In this paper, nonorthogonal principal skewness analysis (NPSA) based unmixing algorithm is proposed. By introducing supersymmetric tensors, the mixed fiber signal unmixing problem is transformed into the skewness analysis problem, and then the non-orthogonal solution is further solved which significantly improves the accuracy of solving pure signal components. The effectiveness of the proposed algorithm is verified by actual data experiments.