Signal identification based on modified filter bank feature and generalized regression neural network for optical fiber perimeter sensing

Abstract

A method based on the modified filter bank (FB) feature and a generalized regression neural network (GRNN) is proposed to raise the accuracy and real-time performance of signal recognition in optical fiber perimeter sensing systems. The FB feature is modified by adding root mean square information of the signal power under the Mel filter bank. Four kinds of sensing signals under three types of weather conditions are obtained experimentally by a fenced perimeter sensing system based on an in-line Sagnac interferometer. The endpoint detection algorithm based on short-term energy is performed on the sensing signals to obtain the effective signal segments. Then the modified FB features of signal segments are extracted and randomly divided into training and testing samples. The optimal GRNN classifier model is generated by performing a 10-fold cross-validation on the training samples and used to recognize the testing samples. The average accuracy can reach 98.22 % and average recognition time is only about 0.07 s. This method is expected to meet the requirements for real-time and accuracy in practical application of optical fiber perimeter sensing signal recognition.