A high-accuracy event discrimination method in optical fiber perimeter security system
-
摘要: 在双马赫曾德光纤周界安防系统中，如何准确高效地实现模式识别仍是一项有待解决的难题。对此提出了一种基于短时傅里叶变换（STFT）与奇异值分解的模式识别方法，实现了三种不同事件类型的准确识别。该方法包含三个步骤：首先，通过对干涉信号进行短时傅里叶变换得到时频信息，根据时频信息找到事件端点并进行滤波；其次，对时频信息进行奇异值分解，根据奇异值的物理意义，由不同行为的奇异值的特点定义特征向量；最后，用支持向量机的方法进行事件分类。为验证方法有效性，搭建了2 km长的围栏系统进行实验验证。进行了攀爬围栏、敲击光缆、晃动围栏三种不同入侵模式下共360组实验，每种入侵行为各120组得到了良好的识别结果（三种事件识别率均在90%以上），提高了系统的信号处理速度，有较高的实际应用价值。Abstract: In the dual Mach-Zehnder perimeter security system, it is difficult to achieve pattern recognition accurately and efficiently. To solve this problem, a pattern recognition method based on short-time Fourier transform(STFT) and singular value decomposition was proposed. In order to realize the accurate distinction of three different events, the method consisted of three steps. Firstly, the time-frequency information was obtained by the short-time Fourier transform of the interference signal, finding the event endpoint and filtering the signal according to the information; And then, the singular value of the time-frequency information was got. The characteristic vectors were defined by the characteristic of singular value, according to the physical meaning of the singular value. Finally, SVM was used to classify the events. In order to verify the effectiveness of the method, a fence system of 2 km long for experiment was built. 360 sets of interference signal data of three events (climbing the fence, knocking the cable, and waggling the fence) were collected, each event had 120 sets of data. The result of experiments demonstrates that the proposed scheme can discriminate three common invasive events with a high recognition rate. The recognition rates of the three events were all above 90%. The scheme also improve signal processing speed of the system, which has high application value.
-
[1] Zhao Lin, Wang Jiqiang, Li Zhen. Optical fiber negative pressure wave pipeline leakage monitoring system[J]. Infrared and Laser Engineering, 2017, 46(7):0722002. (in Chinese) [2] Xu Ning, Dai Ming. Design of distributed optical fiber sensor for temperature and pressure measurement[J]. Chinese Optics, 2017, 46(7):129-134. (in Chinese) [3] Dong Jie. Measurement of underwater acoustic wave using optical distributed acoustic sensing based on space difference[J]. Optics and Precision Engineering, 2017, 25(9):2317-2323. (in Chinese) [4] Chen Q, Liu T, Liu K, et al. An improved positioning algorithm with high precision for dual Mach-Zehnder interferometry disturbance sensing system[J]. Journal of Lightwave Technology, 2015, 33(10):1954-1960. [5] Liu K, Tian M, Jiang J, et al. An improved positioning algorithm in a long-range asymmetric perimeter security system[J]. Journal of Lightwave Technology, 2016, 34(22):5278-5283. [6] Fan Linyong, Jiang Weiwei, Zhao Ruifeng. Temperature characteristic of in-fiber Mach-Zehnder interferometer using twin-core fiber[J]. Optics and Precision Engineering, 2011, 19(1):1-9. (in Chinese) [7] Sun Q, Liu D, Liu H, et al. Distributed disturbance sensor based on a novel Mach-Zehnder interferometer with a fiber-loop[C]//SPIE, 2006, 6344:63440K. [8] Zhang Junnan, Lou Shuqin, Liang Sheng. Study of pattern recognition based on SVM algorithm for -OTDR distributed optical fiber disturbance sensing system[J]. Infrared and Laser Engineering, 2017, 46(4):0422003. (in Chinese) [9] Li Yan, Liang Zhengtao, Li Lijing, et al. Pattern recognition of fiber-optic micro vibration sensor based on wavelet and SVM[J]. Transducer and Microsystem Technologies, 2015, 42(11):1105005. (in Chinese) [10] Zhang Yan, Lou Shuqin, Liang Sheng, et al. Study of pattern recognition based on multi-characteristic parameters for -OTDR distributed optical fiber sensing system[J]. Chinese Journal of Lasers, 2015, 42(11):1105005. (in Chinese) [11] Liu K, Tian M, Liu T, et al. A high-efficiency multiple events discrimination method in optical fiber perimeter security system[J]. Journal of Lightwave Technology, 2015, 33(23):4885-4890. [12] Audone B, Colombo R, Marziali I, et al. The short time Fourier transform and the spectrograms to characterize EMI emissions[C]//International Symposium on Electromagnetic Compatibility-Emc Europe. IEEE, 2016:882-888. [13] Yeap Y M, Ukil A. Fault detection in HVDC system using Short Time Fourier Transform[C]//Power and Energy Society General Meeting. IEEE, 2016. [14] Lebrun M, Leclaire A. An implementation and detailed analysis of the K-SVD image denoising algorithm[J]. Image Processing on Line, 2012, 2(6):96-133. -
点击查看大图
计量
- 文章访问数: 340
- HTML全文浏览量: 58
- PDF下载量: 53
- 被引次数: 0
下载:
