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基于ARMA建模与Sigmoid拟合的光纤周界安防入侵事件识别

Intrusion Event Identification for Fiber Perimeter Security System Based on ARMA Modeling and Sigmoid Fitting

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摘要

在实际的光纤周界安防系统中,既要求判断入侵事件类别,又要求对各类事件发生的可能性做出全面评估。对此提出一种基于自回归滑动平均(ARMA)建模与Sigmoid概率拟合的入侵事件识别方法。在判断入侵事件类别方面,将光纤振动信号的ARMA建模系数与信号自身过零率相结合,构造特征向量,并将其馈入支持向量机(SVM),实现对攀爬、敲击、晃动、剪切、脚踢和撞击6种常见的入侵动作的识别;在评估各类事件的发生可能性方面,引入Sigmoid模型,对训练模式的SVM的各输出值作参数拟合,进而将测试样本的SVM值代入各自Sigmoid模型中完成评估。现场实验表明,该方法对6类常见入侵事件的平均识别率达到87.14%,且可提供各类事件的发生概率参考值,因而具有较高的实用价值。

Abstract

In a practical optical fiber perimeter security system, not only the discrimination of multiple events but also the comprehensive probability evaluation of these events is required. Therefore, this paper proposes a recognition scheme combining autoregressive moving average (ARMA) modeling with Sigmoid probability fitting. In event discrimination, both the ARMA coefficients and the zero-crossing rate of an optical fiber vibration signal are incorporated into a feature vector, which is then fed into a support vector machine (SVM) to recognize six types of common intrusion events: climbing, knocking, waggling, cutting, kicking, and crashing. In comprehensive probability evaluation, the SVM training pattern outputs are used to fit the parameters of a Sigmoid function. Then, the SVM outputs of the test patterns are substituted into this fitted Sigmoid model to yield the expected result. Field experiments reveal that the average recognition rate of six intrusion events by the proposed scheme reaches 87.14%. Moreover, the occurrence probabilities of all intrusion events can be provided as references, thereby presenting vast potential for future applications.

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补充资料

中图分类号:V211

DOI:10.3788/CJL202047.1006004

所属栏目:光纤光学与光通信

基金项目:国家自然科学基金、国家重大科学仪器设备开发专项;

收稿日期:2020-05-06

修改稿日期:2020-06-19

网络出版日期:2013-10-01

作者单位    点击查看

黄翔东:天津大学电气自动化与信息工程学院, 天津 300072
王碧瑶:天津大学电气自动化与信息工程学院, 天津 300072
刘琨:天津大学精密仪器与光电子工程学院, 天津 300072
刘铁根:天津大学精密仪器与光电子工程学院, 天津 300072

联系人作者:黄翔东(beiyangkl@tju.edu.cn); 王碧瑶(beiyangkl@tju.edu.cn); 刘琨(beiyangkl@tju.edu.cn);

备注:国家自然科学基金、国家重大科学仪器设备开发专项;

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引用该论文

Huang Xiangdong,Wang Biyao,Liu Kun,Liu Tiegen. Intrusion Event Identification for Fiber Perimeter Security System Based on ARMA Modeling and Sigmoid Fitting[J]. Chinese Journal of Lasers, 2020, 47(10): 1006004

黄翔东,王碧瑶,刘琨,刘铁根. 基于ARMA建模与Sigmoid拟合的光纤周界安防入侵事件识别[J]. 中国激光, 2020, 47(10): 1006004

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