基于DBO-ICEEMDAN-NLM的光纤周界入侵信号去噪

doi:10.12305/j.issn.1001-506X.2025.12.03

摘要/Abstract

摘要：

针对复杂环境下光纤周界入侵告警系统受噪声干扰难以对入侵事件识别和定位的问题，提出基于蜣螂优化(dung beetle optimizer，DBO)算法优化改进的自适应噪声完全集合经验模态分解（improved complete ensemble empirical mode decomposition with adaptive noise，ICEEMDAN）结合非局部均值（non-local mean，NLM）滤波的振动信号去噪方法。首先利用DBO-ICEEMDAN对信号分解获得若干本征模态函数（intrinsic mode function，IMF），然后引入样本熵对IMF分量的复杂程度进行判决，最后将NLM滤波后的信噪混合分量和低频信号分量进行重构完成对信号的去噪。通过振动仿真信号验证，并对光纤周界双Mach-Zehnder系统实测的敲击、攀爬和跑动信号去噪。以敲击信号为例，与变分模态分解-排列熵和NLM滤波-自适应噪声完备集合经验模态分解-小波阈值去噪方法相比，所提方法的噪声抑制比分别提高了2.94 dB和1.08 dB，均方根误差分别降低了72.41%和57.89%，在更好地滤除信号噪声的同时充分保留了信号的关键特征。

关键词: 光纤传感, 信号去噪, 蜣螂优化算法, 经验模态分解, 非局部均值滤波

Abstract:

A vibration signal denoising method based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) optimized by the dung beetle optimizer (DBO) algorithm, combined with non local mean (NLM) filtering is proposed to address the problem of difficulty in identifying and locating of intrusion events in optical fiber perimeter intrusion alarm systems under noise interference in complex environments. Firstly, DBO-ICEEMDAN is used to decompose the signal and obtain several intrinsic mode function (IMF). Then, sample entropy is introduced to determine the complexity of the IMF components. Finally, the NLM filtered signal-to-noise mixture and low-frequency signal components are reconstructed to complete the denoising of the signal. Verify through vibration simulation signals and denoise the impact, climbing, and running signals measured in the optical fiber perimeter dual Mach-Zehnder system. Taking the tapping signal as an example, compared with the variational mode decomposition-permutation entropy and NLM filtering-adaptive noise complete set empirical mode decomposition wavelet threshold denoising methods, the proposed method improves the noise suppression ratio by 2.94 dB and 1.08 dB respectively, and reduces the root mean square error by 72.41% and 57.89% respectively. The proposed method effectively filters out signal noise while fully preserving the key features of the signal.

Key words: optical fiber sensing, signal denoising, dung beetle optimizer (DBO) algorithm, empirical mode decomposition (EMD), non-local mean filter

中图分类号:

TP 212

马愈昭, 吕其明, 李猛. 基于DBO-ICEEMDAN-NLM的光纤周界入侵信号去噪[J]. 系统工程与电子技术, 2025, 47(12): 4024-4033.

Yuzhao MA, Qiming LYU, Meng LI. Optical fiber perimeter intrusion signal denoising based on DBO-ICEEMDAN-NLM[J]. Systems Engineering and Electronics, 2025, 47(12): 4024-4033.

图/表 17

图1

图2

图3

图4

图5

图6

图7

表1

图8

图9

图10

图11

图12

图13

图14

图15

表2

参考文献 30

1	PENDAO C, SILVA I. Optical fiber sensors and sensing networks: overview of the main principles and applications[J]. Sensors, 2022, 22 (19): 7554. doi: 10.3390/s22197554
2	MAHMOUD S S. Practical aspects of perimeter intrusion detection and nuisance suppression for distributed fiber-optic sensors[J]. IEEE Trans. on Instrumentation and Measurement, 2023, 72, 2517311.
3	LI S Z, PENG R Z, LIU Z L, et al. Perimeter monitoring of urban buried pipeline threated by construction activities based on distributed fiber optic sensing and real-time object detection[J]. Optics Express, 2024, 32 (2): 2590- 2606. doi: 10.1364/OE.509487
4	LIU K, JIN X B, JIANG J F, et al. Interferometer-based distributed optical fiber sensors in long-distance vibration detection: a review[J]. IEEE Sensors Journal, 2022, 22 (22): 21428- 21444. doi: 10.1109/JSEN.2022.3213036
5	LIAO Y H, LI K, GONG Y D. Research on the noise suppression by φ-OTDR[J]. Journal of Optics, 2024, 53, 4721- 4730. doi: 10.1007/s12596-023-01600-4
6	BARANGER J, AGUET J, VILLEMAIN O. Fast thresholding of SVD clutter filter using the spatial similarity matrix and a sum-table algorithm[J]. IEEE Trans. on Ultrasonics, Ferroelectrics, and Frequency Control, 2023, 70 (8): 821- 830. doi: 10.1109/TUFFC.2023.3289235
7	WANG J, LIU Z, WANG Y, et al. Extraction of strain characteristic signals from wind turbine blades based on EEMD-WT[J]. Energy Engineering, 2023, 120 (5): 1149- 1162. doi: 10.32604/ee.2023.025209
8	马愈昭, 刘逵, 张岩峰, 等. CEEMD结合改进小波阈值的激光雷达信号去噪算法[J]. 系统工程与电子技术, 2023, 45 (1): 93- 100.
	MA Y Z, LIU K, ZHANG Y F, et al. Laser radar signal denoising algorithm based on CEEMD combined with improved wavelet threshold[J]. Systems Engineering and Electronics, 2023, 45 (1): 93- 100.
9	ZHANG C C, SHI Y N, LIU J G, et al. A denoising method of mine microseismic signal based on NAEEMD and frequency-constrained SVD[J]. The Journal of Supercomputing, 2022, 78 (15): 17095- 17113. doi: 10.1007/s11227-022-04554-9
10	于淼, 张耀鲁, 何禹潼, 等. 变分模态分解-排列熵方法用于分布式光纤振动传感系统去噪[J]. 光学学报, 2022, 42 (7): 62- 73.
	YU M, ZHANG Y L, HE Y T, et al. Variational mode decomposition and permutation entropy method for denoising of distributed optical fiber vibration sensing system[J]. Acta Optica Sinica, 2022, 42 (7): 62- 73.
11	WANG Y W, CHEN P, ZHAO Y M, et al. A denoising method for mining cable PD signal based on genetic algorithm optimization of VMD and wavelet threshold[J]. Sensors, 2022, 22 (23): 9386. doi: 10.3390/s22239386
12	冯帅, 曹茹茹, 马愈昭, 等. 改进CEEMDAN结合新型小波变换的激光雷达去噪算法[J]. 系统工程与电子技术, 2024, 46 (12): 4054- 4061.
	FENG S, CAO R R, MA Y Z, et al. Lidar denoising algorithm of improved CEEMDAN combined with novel wavelet change[J]. Systems Engineering and Electronics, 2024, 46 (12): 4054- 4061.
13	TORRES M E, COLOMINAS M A, SCHLOTTHAUER G, et al. A complete ensemble empirical mode decomposition with adaptive noise[C]//Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2011: 4144−4147.
14	章芳情, 袁方, 贺玉, 等. 基于NLM- CEEMDAN和样本熵的水电机组振动信号去噪[J]. 中国农村水利水电, 2023 (6): 286- 294.
	ZHANG F Q, YUAN F, HE Y, et al. Vibration signal de-noising of hydropower units based on NLM-CEEMDAN and sample entropy[J]. China Rural Water and Hydropower, 2023 (6): 286- 294.
15	GEETHA K, HOTA M K. Microseismic signal denoising based on variational mode decomposition with adaptive non-local means filtering[J]. Pure and Applied Geophysics, 2023, 180 (11): 3709- 3731. doi: 10.1007/s00024-023-03365-0
16	XU L, SU H Z, CAI D S, et al. RDTS noise reduction method based on ICEEMDAN-FE-WSTD[J]. IEEE Sensors Journal, 2022, 22 (18): 17854- 17863. doi: 10.1109/JSEN.2022.3196944
17	YUAN D S, LIU F, YIN Z G, et al. Fault diagnosis of motor bearing based on the SCNGO-ICEEMDAN method[C]//Proc. of the IEEE 7th International Electrical and Energy Conference, 2024: 4225-4231.
18	LI K, WU H, LIU X M, et al. Intelligent diagnosis of rolling bearing based on ICEEMDAN-WTD of noise reduction and multi-strategy fusion optimization SCNs[J]. IEEE Access, 2024, 12, 36908- 36923. doi: 10.1109/ACCESS.2024.3373554
19	TANG J, LIU G, PAN Q T. A review on representative swarm intelligence algorithms for solving optimization problems: applications and trends[J]. IEEE/CAA Journal of Automatica Sinica, 2021, 8 (10): 1627- 1643. doi: 10.1109/JAS.2021.1004129
20	EMAMBOCUS B A S, JASSER M B, AMPHAWAN A. A survey on the optimization of artificial neural networks using swarm intelligence algorithms[J]. IEEE Access, 2023, 11, 1280- 1294. doi: 10.1109/ACCESS.2022.3233596
21	COLOMINAS M A, SCHLOTTHAUER G, TORRES M E. Improved complete ensemble EMD: a suitable tool for biomedical signal processing[J]. Biomedical Signal Processing and Control, 2014, 14 (6): 19- 29.
22	XUE J K, SHEN B. Dung beetle optimizer: a new meta-heuristic algorithm for global optimization[J]. The Journal of Supercomputing, 2023, 79, 7305- 7336. doi: 10.1007/s11227-022-04959-6
23	MARQUES J A L, CORTEZ P C, MADEIRO J P V, et al. Nonlinear characterization and complexity analysis of cardiotocographic examinations using entropy measures[J]. The Journal of Supercomputing, 2020, 76 (2): 1305- 1320. doi: 10.1007/s11227-018-2570-8
24	CHEN J Y, ZHOU D, LYU C, et al. An integrated method based on CEEMD-SampEn and the correlation analysis algorithm for the fault diagnosis of a gearbox under different working conditions[J]. Mechanical Systems and Signal Processing, 2018, 113 (8): 102- 111.
25	KUMAR S, PANIGRAHY D, SAHU P K. Denoising of electrocardiogram （ECG） signal by using empirical mode decomposition （EMD） with non-local mean （NLM） technique[J]. Biocybernetics and Biomedical Engineering, 2018, 38 (2): 297- 312. doi: 10.1016/j.bbe.2018.01.005
26	SINGH P, PRADHAN G. Variational mode decomposition based ECG denoising using non-local means and wavelet domain filtering[J]. Australasian Physical & Engineering Sciences in Medicine, 2018, 41 (4): 891- 904.
27	VAN M, KANG H J, SHIN K S. Rolling element bearing fault diagnosis based on non-local means denoising and empirical mode decomposition[J]. IET Science, Measurement & Technology, 2014, 8(6): 571−578.
28	XUE J K, SHEN B. A novel swarm intelligence optimization approach: sparrow search algorithm[J]. Systems Science & Control Engineering, 2020, 8 (1): 22- 34.
29	KENNEDY J, EBERHART R. Particle swarm optimization[C]//Proc. of the International Conference on Neural Networks, 1995: 1942−1948.
30	MA C Y, LIU T G, LIU K, et al. Long-range distributed fiber vibration sensor using an asymmetric dual Mach-Zehnder interferometers[J]. Journal of Lightwave Technology, 2016, 34 (9): 2235- 2239. doi: 10.1109/JLT.2016.2532877

去噪方法	SNR/dB	RMSE
VMD-PE	22.33	0.31
NLM-CEEMDAN-WT	27.74	0.18
DBO-ICEEMDAN-NLM	30.18	0.08

去噪方法	敲击信号			攀爬信号			跑动信号
去噪方法	D_nSNR/dB	NRR/dB	RMSE	D_nSNR/dB	NRR/dB	RMSE	D_nSNR/dB	NRR/dB	RMSE
VMD-PE	31.31	3.63	0.29	33.13	3.92	0.20	32.24	3.82	0.33
NLM-CEEMDAN-WT	28.97	5.49	0.19	31.54	5.38	0.11	31.01	4.54	0.21
DBO-ICEEMDAN-NLM	26.44	6.57	0.08	28.15	6.08	0.03	27.17	5.89	0.09

[1]	王全全, 谢松霖, 顾志豪, 吴城坤, 张更新. 低信噪比下基于ICEEMDAN和HHO的协作频谱感知方法[J]. 系统工程与电子技术, 2025, 47(9): 3109-3116.
[2]	张本辉, 刘松涛, 晁玉龙. 基于DBO-DAOD的未知雷达调制方式识别算法[J]. 系统工程与电子技术, 2025, 47(6): 1833-1842.
[3]	曾顶, 殷君君, 杨健. 基于融合距离的极化SAR图像非局部均值滤波[J]. 系统工程与电子技术, 2024, 46(5): 1493-1502.
[4]	王伟, 杜旭洋, 杨志伟, 吴凡. 基于无人艇的导航雷达目标检测跟踪算法[J]. 系统工程与电子技术, 2024, 46(5): 1561-1572.
[5]	隋东, 杨振宇, 丁松滨, 周婷婷. 基于EMSDBO算法的无人机三维航迹规划[J]. 系统工程与电子技术, 2024, 46(5): 1756-1766.
[6]	冯帅, 曹茹茹, 马愈昭, 丁超. 改进CEEMDAN结合新型小波变换的激光雷达去噪算法[J]. 系统工程与电子技术, 2024, 46(12): 4054-4061.
[7]	赵云龙, 孙骞, 简鑫, 李一兵, 于飞. 基于改进EMD-CIIT降噪算法的INS/5G组合导航方法[J]. 系统工程与电子技术, 2024, 46(12): 4203-4212.
[8]	薛锡瑞, 黄树彩, 韦道知, 吴建峰. 基于EMD-DESN的无人机集群航迹目的地预测[J]. 系统工程与电子技术, 2024, 46(1): 290-299.
[9]	吴宗收, 汪立新, 沈强, 李灿, 李文华. 基于CEEMD半球谐振陀螺输出预测方法[J]. 系统工程与电子技术, 2023, 45(10): 3259-3264.
[10]	马愈昭, 刘逵, 张岩峰, 冯帅, 熊兴隆. CEEMD结合改进小波阈值的激光雷达信号去噪算法[J]. 系统工程与电子技术, 2023, 45(1): 93-100.
[11]	时艳玲, 刘子鹏, 张学良, 顾为亮. 基于EMD能量占比的海面漂浮小目标特征检测[J]. 系统工程与电子技术, 2021, 43(2): 300-310.
[12]	彭正红, 杨德贵, 王行, 王浩, 朱政亮. 基于趋势估计的微多普勒分离与特征提取算法[J]. 系统工程与电子技术, 2021, 43(12): 3452-3461.
[13]	张丹娜, 钱锋, 冯辉, 闻年成. Walsh码软扩频信号降噪算法[J]. 系统工程与电子技术, 2020, 42(4): 773-780.
[14]	蒋铁军, 张怀强, 周成杰. 事件影响下的时间序列多尺度集成预测[J]. 系统工程与电子技术, 2020, 42(2): 390-397.
[15]	安影, 范训礼, 陈莉, 刘佩. 结合FABEMD和改进的显著性检测的图像融合[J]. 系统工程与电子技术, 2020, 42(2): 292-300.