Optical fiber sensing recognition algorithm based on deep neural network

In order to solve the problem of reducing the recognition probability caused by the aliasing of different types of event signals in the optical fiber sensing process, a dual optical fiber sensing structure using differential correlation calculation is built. On this basis, a signal recognition algorithm based on deep neural network is proposed. First, the echo signal of the dual fiber is used to calculate the correlation coefficient. Then, the threshold range is set by signal characteristics of different event types, so as to improve the signal-to-noise ratio through correlation calculation and threshold filtering. A deep neural network model with three hidden layers is designed, and the purpose of low-frequency noise suppression and signal aliasing demodulation is accomplished by separating the input layer and the related operation layer. The experiments separately test three common intrusion events. The recognition probability of combined events by different algorithms is analyzed. The results show that the echo spectrum shape of the three events has significant characteristics. The recognition probability of the three algorithms is more than 95% for a single trigger event, and the average recognition value of this algorithm is 98.5%. When two events are triggered at the same time, the average recognition probabilities of the three algorithms are 73.4%, 84.5%, and 96.4%, respectively. When three events are triggered at the same time, the average recognition probabilities of the three algorithms are 65.2%, 78.3%, and 93.5%, respectively. It can be seen that this algorithm has a better recognition effect when there is interference and aliasing of signals in optical fiber sensing.