改进经验模态分解算法在光纤布拉格光栅周界入侵行为分类中的应用
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摘要
为了解决周界入侵行为识别正确率低的问题,对经验模态分解算法进行改进,并将其用于光纤布拉格光栅周界入侵行为分类。该方法利用短时平均过零率从整体信号中提取入侵信号,采用两次极值波延拓抑制经验模态分解算法的端点效应,对入侵信号进行分解并提取有效分量的特征,引用支持向量机对入侵行为进行识别;在室外环境下分别对无入侵和攀爬、剪切、碰撞、触摸4种入侵行为进行分类与识别。结果表明,所提方法能有效识别不同的入侵行为,识别正确率大于96%。
To solve the problem of low recognition rate of perimeter intrusion behaviors, an improved empirical mode decomposition algorithm is used in the perimeter intrusion behaviors classification of fiber Bragg gratings. In this algorithm, the intrusion signal is extracted from the overall signal by using the short time average zero-crossing rate algorithm, and the double extreme wave prolongation is used to decompose the end effect of empirical mode decomposition algorithm. The improved algorithm is employed to decompose the intrusion signal and the characteristics of the effective components are extracted. Support vector machine is used to identify the intrusion behaviors. The nonintrusive behavior and four different invasion behaviors such as climbing, shearing, colliding, and touching are used to classify and recognize in outdoor environment. The results show that the proposed method can effectively identify different intrusion behaviors, and the recognition rate is greater than 96%.
To solve the problem of low recognition rate of perimeter intrusion behaviors, an improved empirical mode decomposition algorithm is used in the perimeter intrusion behaviors classification of fiber Bragg gratings. In this algorithm, the intrusion signal is extracted from the overall signal by using the short time average zero-crossing rate algorithm, and the double extreme wave prolongation is used to decompose the end effect of empirical mode decomposition algorithm. The improved algorithm is employed to decompose the intrusion signal and the characteristics of the effective components are extracted. Support vector machine is used to identify the intrusion behaviors. The nonintrusive behavior and four different invasion behaviors such as climbing, shearing, colliding, and touching are used to classify and recognize in outdoor environment. The results show that the proposed method can effectively identify different intrusion behaviors, and the recognition rate is greater than 96%.
引文
[1] Chen Y, Zhou L X, Liu H L. A fiber Bragg grating sensor perimeter intrusion localization method optimized by improved particle swarm optimization algorithm[J]. IEEE Sensors Journal, 2018, 18(3): 1243-1249.
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[13] Seifpour S, Niknazar H, Mikaeili M, et al. A new automatic sleep staging system based on statistical behavior of local extrema using single channel EEG signal[J]. Expert Systems with Applications, 2018, 104: 277-293.
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[15] Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society A, 1998, 454(1971): 903-995.
[16] Li P. Method based on extension of extreme wave in dealing with end effect[J]. Systems Engineering and Electronics, 2016, 38(9): 1987-1992. 李沛. 基于极值波延拓的端点效应处理方法[J]. 系统工程与电子技术, 2016, 38(9): 1987-1992.
[17] Wu H Y, Jia B, Bian P. Study on endpoint detection technology based on fiber perimeter security system[J]. Chinese Journal of Scientific Instrument, 2013, 34(4): 743-748. 吴红艳, 贾波, 卞庞. 光纤周界安防系统端点检测技术的研究[J]. 仪器仪表学报, 2013, 34(4): 743-748.
[18] Ma P F, Liu K, Jiang J F, et al. Probabilistic event discrimination algorithm for fiber optic perimeter security systems[J]. Journal of Lightwave Technology, 2018, 36(11): 2069-2075.
[19] Zhu C H, Zhao Y, Wang J P, et al. Ensemble recognition of fiber intrusion behavior based on blending features[J]. Opto-Electronic Engineering, 2016, 43(12): 6-12. 朱程辉, 赵益, 王建平, 等. 光纤入侵行为融合特征的集成识别[J]. 光电工程, 2016, 43(12): 6-12.
[20] Guo Y Q, Jia X P, Paull D. Effective sequential classifier training for SVM-based multitemporal remote sensing image classification[J]. IEEE Transactions on Image Processing, 2018, 27(6): 3036-3048.
[2] Huang X D, Zhang H J, Liu K, et al. High-efficiency intrusion recognition by using synthesized features in optical fiber perimeter security system[J]. Acta Physica Sinica, 2017, 66(12): 124206. 黄翔东, 张皓杰, 刘琨, 等. 基于综合特征的光纤周界安防系统高效入侵事件识别[J]. 物理学报, 2017, 66(12): 124206.
[3] Yu J J, Zhang J W. Recent progress on high-speed optical transmission[J]. Digital Communications and Networks, 2016, 2(2): 65-76.
[4] Weng G X, Xu X H. Blind separation of radar signals based on detection of time frequency single source point[J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2017, 29(1): 42-48. 翁国秀, 徐学红. 基于时频单源点检测的雷达信号的盲分选[J]. 重庆邮电大学学报(自然科学版), 2017, 29(1): 42-48.
[5] Wen H, Zhang J H, Yao W X, et al. FFT-based amplitude estimation of power distribution systems signal distorted by harmonics and noise[J]. IEEE Transactions on Industrial Informatics, 2018, 14(4): 1447-1455.
[6] Chen Y, Cheng Y N, Liu H L. Application of improved wavelet adaptive threshold de-noising algorithm in FBG demodulation[J]. Optik, 2017, 132: 243-248.
[7] Chen Y, Wu C T, Liu H L. EMD self-adaptive selecting relevant modes algorithm for FBG spectrum signal[J]. Optical Fiber Technology, 2017, 36: 63-67.
[8] Dong B, Hao J Z, Paulose V. Armored-cable-based FBG security fence for perimeter intrusion detection with higher performance[J]. Sensors and Actuators A, 2012, 180: 15-18.
[9] Pan P, Xi L X, Zhang X G, et al. Experimental research on polarization mode dispersion measurement based on empirical mode decomposition[J]. Chinese Journal of Lasers, 2018, 45(1): 0106002. 潘潘, 席丽霞, 张晓光, 等. 基于经验模态分解的偏振模色散测量实验研究[J]. 中国激光, 2018, 45(1): 0106002.
[10] Cheng L Y, Mi G Y, Li S, et al. Defects diagnosis in laser brazing using near-infrared signals based on empirical mode decomposition[J]. Optics & Laser Technology, 2018, 100: 12-20.
[11] Li X Q, Chen W J, Su X Y. Three dimensional reconstruction based on improved empirical mode decomposition[J]. Journal of Sichuan University (Natural Science Edition), 2018, 55(1): 111-117. 李绪琴, 陈文静, 苏显渝. 基于改进经验模态分解的三维重建[J]. 四川大学学报(自然科学版), 2018, 55(1): 111-117.
[12] Guo D, Shen Y Y, Yin B. Research on method for end effects reduction of empirical mode decomposition[J]. Computer Technology and Development, 2016, 26(3): 89-92, 96. 郭迪, 沈洋洋, 尹兵. 经验模式分解端点效应抑制方法的研究[J]. 计算机技术与发展, 2016, 26(3): 89-92, 96.
[13] Seifpour S, Niknazar H, Mikaeili M, et al. A new automatic sleep staging system based on statistical behavior of local extrema using single channel EEG signal[J]. Expert Systems with Applications, 2018, 104: 277-293.
[14] Sch?lkopf B, Smola A J, Williamson R C, et al. New support vector algorithms[J]. Neural Computation, 2000, 12(5): 1207-1245.
[15] Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society A, 1998, 454(1971): 903-995.
[16] Li P. Method based on extension of extreme wave in dealing with end effect[J]. Systems Engineering and Electronics, 2016, 38(9): 1987-1992. 李沛. 基于极值波延拓的端点效应处理方法[J]. 系统工程与电子技术, 2016, 38(9): 1987-1992.
[17] Wu H Y, Jia B, Bian P. Study on endpoint detection technology based on fiber perimeter security system[J]. Chinese Journal of Scientific Instrument, 2013, 34(4): 743-748. 吴红艳, 贾波, 卞庞. 光纤周界安防系统端点检测技术的研究[J]. 仪器仪表学报, 2013, 34(4): 743-748.
[18] Ma P F, Liu K, Jiang J F, et al. Probabilistic event discrimination algorithm for fiber optic perimeter security systems[J]. Journal of Lightwave Technology, 2018, 36(11): 2069-2075.
[19] Zhu C H, Zhao Y, Wang J P, et al. Ensemble recognition of fiber intrusion behavior based on blending features[J]. Opto-Electronic Engineering, 2016, 43(12): 6-12. 朱程辉, 赵益, 王建平, 等. 光纤入侵行为融合特征的集成识别[J]. 光电工程, 2016, 43(12): 6-12.
[20] Guo Y Q, Jia X P, Paull D. Effective sequential classifier training for SVM-based multitemporal remote sensing image classification[J]. IEEE Transactions on Image Processing, 2018, 27(6): 3036-3048.