A denoising method based on singular value decomposition (SVD) with particle swarm optimization (PSO) is proposed to improve the signal-to-noise ratio (SNR) of far-end disturbances in distributed sensor system based… Click to show full abstract
A denoising method based on singular value decomposition (SVD) with particle swarm optimization (PSO) is proposed to improve the signal-to-noise ratio (SNR) of far-end disturbances in distributed sensor system based on phase-sensitive optical time-domain reflectometry ($\Phi $ -OTDR). Also, an improved clustering algorithm is introduced to locate the position of disturbance. The effective sensing distance of the $\Phi $ -OTDR system is 25.05 km and four kinds of disturbance events, including watering, knocking, climbing and pressing, are applied on the sensing fiber respectively. A series of experiments of single-point far-end disturbances and five-point disturbances are carried out. Experimental results demonstrate that the SNR of far-end disturbance can be effectively improved to over 12dB, the processing time is less than 3 seconds, and the average location accuracy rate is more than 96%. Compared with the commonly used denoising methods, such as empirical mode decomposition (EMD), wavelet-1D and wavelet-2D, the SVD denoising with PSO method has better performance in SNR enhancement and real-time, which is beneficial for accurate positioning.
