Adaptive Filter for Automatic Identification of Multiple Faults in a Noisy OTDR Profile

TL;DR

This paper introduces a new low-complexity, parameter-free filtering method for accurately detecting multiple fault-related level shifts in noisy OTDR profiles, outperforming existing techniques.

Contribution

A novel two-stage regularization filtering approach for automatic, fast, and accurate identification of multiple faults in noisy OTDR data.

Findings

Outperforms current methods in fault detection accuracy

Requires low computational resources

Easily implementable in dedicated hardware

Abstract

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.