Improved perturbation detection in direct detected φ-OTDR systems using matched filtering

Abstract

Nuisance Alarm Rate (NAR) is critical in φ-OTDR perturbation detection systems. We present in this letter a novel matched filtering-based feature extractor which aims to noise reduction so that the detection system gets improved performance. This feature extractor requires a small number of data vectors to be acquired which is combined with a random forest-based machine learning strategy to significantly reduce the NAR. In addition, since the number of data vectors is small, this system can also be useful for time-sensitive detection applications.