Adaptive Filter for Automatic Identification of Multiple Faults in a Noisy OTDR Profile
This is an incremental improvement for optical time-domain reflectometry (OTDR) systems, enabling more efficient fault detection in noisy environments.
The paper tackles the problem of identifying multiple faults in noisy OTDR profiles by developing a two-stage regularization filter that distinguishes meaningful level shifts from signal fluctuations, achieving fast detection with low computational effort and outperforming current methods in case studies.
We present a novel methodology able to distinguish meaningful level shifts from typical signal fluctuations. A two-stage regularization filtering can accurately identify the location of the significant level-shifts with an efficient parameter-free algorithm. The developed methodology demands low computational effort and can easily be embedded in a dedicated processing unit. Our case studies compare the new methodology with current available ones and show that it is the most adequate technique for fast detection of multiple unknown level-shifts in a noisy OTDR profile.