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

Lu H.; Fang N.; Wang L.

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

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

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? 2022 Elsevier Inc.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.

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《Optical fiber technology》 |2022年第9期|1.1-1.8|共8页
作者
Lu H.; Fang N.; Wang L.;
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作者单位

Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai University;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
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关键词
Endpoint detection; Filter bank feature; Generalized regression neural network; Optical fiber perimeter sensing system; Signal recognition;

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

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