Novel signal processing techniques for fiber optic distributed acoustic sensing

In this thesis, to reduce the effect of noise in phase-sensitive optical time domain based (φ-OTDR) distributed acoustic sensing (DAS) systems, two novel approaches are proposed and a real experimental φ-OTDR system is developed for validation. The first approach is the temporal adaptive processing of φ-OTDR signals which is based on maximizing the signal-to-noise ratio (SNR) at the output of an adaptive linear filter. When the vibration frequency of interest is known a priori, it is called the adaptive matched filter (AMF). The second approach is based on the largest eigenvalue computation of the optical covariance matrix which does not require any prior information about the vibration frequencies. Both methods utilize the correlation properties of the measured data. In the first method, the noise covariance matrix is estimated to compute an adaptive weight vector for optimum linear filtering. In the second method, the eigenvalues of the covariance matrix are computed and the maximum eigenvalue is used as the test statistic for detecting the vibrations along the fiber optic cable route. This so called maximum eigenvalue detection (MED) technique is assisted by the random matrix theory (RMT) to establish the binary detection threshold. First, the efficacy of the proposed methods was demonstrated with Monte Carlo simulations. In the second phase, a φ-OTDR system was developed in the laboratory to gather real data and to verify the AMF and MED techniques with indoor experiments. In the last phase, extensive field tests, with both buried fibers and fibers on fences, were carried out to validate the proposed techniques in real-world conditions. The results show that more than 20 dB of SNR values can be achieved without any reduction in the system bandwidth and using any optical amplifier stage in the hardware.