The invention discloses a method for identifying and detecting disturbance signals of a phase-sensitive
optical time domain reflectometer. The method mainly aims at improving detection and intelligent identification ability of the phase-sensitive
optical time domain reflectometer to external disturbance, and reducing a misstatement rate and a
false alarm rate of a
system in a practical complicated
noise environment. The method comprises the steps that longitudinal
time sequence signals of various spatial points serve as
processing objects; fractal characteristics of the longitudinal
time sequence signals are extracted for disturbance detection and positioning; multi-scale
decomposition is conducted on the
time sequence signals by
wavelet transformation; the energy characteristics of detail
signal components under different scales are extracted by utilizing distribution differences of the different-type disturbance signals of time-varying interference signals such as fluctuating background noises and sound
waves, real intrusion signals and the like on a multi-scale time frequency shaft; multi-scale
time frequency distribution characteristic vectors of the signals are formed; and specific attributes of the disturbance signals are identified and classified by combining a back-propagation (BP)
neural network identification method. The method is applicable to application fields of
perimeter security and protection, long-distance pipeline security, large-
scale structure health monitoring and the like.