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Phase-sensitive optical time domain reflectometry (Φ-OTDR) is easy to be interfered by ambient noises, and the nonlinear coherent addition of different interferences always makes it difficult to detect real human intrusions and causes high nuisance alarm rates (NARs) in practical applications. In this paper, an effective temporal signal separation and determination method is proposed to improve its detection performance in complicated noisy environments. Unlike the conventional analysis of transverse spatial signals, the time-evolving sensing signal of Φ-OTDR system is at first obtained for each spatial point by accumulating the changing OTDR traces at different moments. Then, its longitudinal temporal signal is decomposed and analyzed by a multi-scale wavelet decomposition method. By selectively recombining the corresponding scale components, it can effectively extract human intrusion signals, and separate the influences of slow change of the system and other environmental interferences. Compared with the conventional differentiation way and fast Fourier transformation denoising method, the SNRs of the detecting signals for the proposed method is always the best, which can be raised by up to ~35 dB for the best case. Moreover, from the decomposed components, different event signals can be effectively determined by their energy distribution features, and the NAR can be controlled to be less than 2% in the test.
Wu et al. (Fri,) studied this question.