An Improved OTDR Signal Denoising Method Based on ICEEMDAN-NLM Algorithm

Optical time-domain reflectometer (OTDR) is a key device for diagnosing the health of optical fiber links. The backscattered signals it relies on are inevitably affected by noise during transmission, especially when the signal is weak and severely disturbed, making it difficult to identify events in the test curve. Consequently, filtering out noise from these signals to facilitate subsequent fault localization and event recognition has emerged as a critical problem to solve. To address this issue, a novel OTDR signal denoising method has been proposed, combining an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) with nonlocal means (NLM) filtering. This approach classifies the intrinsic mode function (IMF) components derived from ICEEMDAN based on their sample entropy values, reconstructs the divided signal components, and further filters out high-frequency noise components using the NLM algorithm, to complete the effective denoising of the OTDR signal. Comparative analyses through simulations and practical examples show that this method outperforms traditional denoising techniques such as soft and hard wavelet thresholding, improved wavelet thresholding, NLM, and ICEEMDAN-improved wavelet thresholding in terms of denoising effectiveness. The proposed method has significant improvements/reductions in signal-to-noise ratio (SNR)/root mean square error (RMSE) and provides a new approach for noise reduction in OTDR signals.