Signal-to-noise ratio analysis of computational distributed fiber-optic sensing

In this work, we analyze the signal-to-noise ratio of the computational distributed fiber-optic sensing technique via differential ghost imaging in the time domain using the illumination pattern of Walsh-Hadamard sequences instead of random sequences. When only the white Gaussian noise is considered in the detection, both the theoretical and experimental results show that the computational method requires twice more number of averages compared to the conventional time-domain method in order to achieve the same level of signal-to-noise ratio. Since the computational approach is focusing on stationary measurement, doubling the measurement time can normally be acceptable in practice, but it can reduce the sampling rate requirement significantly compared to the conventional method, offering great advantage to simplify the data acquisition design in the distributed fiber-optic sensing system.