Systems Engineering and Electronics ›› 2025, Vol. 47 ›› Issue (12): 4024-4033.doi: 10.12305/j.issn.1001-506X.2025.12.03

• Electronic Technology • Previous Articles    

Optical fiber perimeter intrusion signal denoising based on DBO-ICEEMDAN-NLM

Yuzhao MA1,*, Qiming LYU1, Meng LI2   

  1. 1. Tianjin Key Laboratory for Intelligent Signal and Image Processing,Civil Aviation University of China,Tianjin 300300,China
    2. College of Air Traffic Management,Civil Aviation University of China,Tianjin 300300,China
  • Received:2024-11-18 Revised:2025-02-21 Online:2025-03-13 Published:2025-03-13
  • Contact: Yuzhao MA

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

CLC Number: 

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