Optical Fiber Intrusion Signal Recognition Based on Improved Mel Frequency Cepstrum Coefficient

Abstract

Oil and gas resources pipelines, boundary lines and other places need to monitor their safety status in real time. The fiber early warning system becomes a good choice for its high sensitivity, corrosion resistance and concealment. The system provides early warning of the detection of fiber vibration signals. In this paper, an improved Mel frequency cepstrum coefficient (MFCC) method is proposed for the cepstrum characteristics recognition of different typical optical fiber vibration signals. Firstly, we pre-process the intrusion signals and obtain its power spectral density (PSD) to quantify the difference of frequency spectrum in respective intrusions. Secondly, the adaptive filter bank is designed according to the distribution of signal power spectrum to improve the conventional MFCC method. Through the analysis of the characteristic parameters, the MFCC coefficients are obtained. Finally, the Mean-crossing rates (MCR) of MFCC are calculated and the appropriate thresholds are selected to classify the typical vibration signals. Compared with the traditional MFCC, this improved MFCC method realizes adaptive division of frequency band according to the distribution of signal power spectrum. Experiments show that the algorithm can identify the manual signal, the mechanical signal and the vehicle signal in the research of the vibration signal recognition of the optical fiber pre-warning system (OFPS).