Processing Optical Fiber Sensing Signals with Improved Compressed Sensing Algorithm

A self-balancing orthogonal matching tracking (SbOMP) algorithm based on compression perception (CS) orthogonal matching tracking (OMP) is proposed for the harsh and complex environment of underground pipe gallery and the large noise interference. The prerequisite of the CS reconstruction algorithm is that the sparseness is known. This study uses projection tracking method for multi-indicator fusion and the maximum inflection point to determine the sparseness. The number of atoms selected for each iteration is determined by self-balancing the relationship between the observation matrix and the correlation between the residual signal in the iteration. The simulation results show that the improved algorithm can retain the main characteristics of the signal and have the least processing time. Compared with the GtOMP algorithm, the running time is reduced by about 5.9% to 12%.