Improved perturbation detection in direct detected ϕ-OTDR systems using a novel matched filtering
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 vec...
| Authors: | ; ; ; |
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| Format: | Article |
| Publication date: | 2019 |
| Country: | España |
| Institution: | Universidad de Alcalá (UAH) |
| Repository: | e_Buah Biblioteca Digital Universidad de Alcalá |
| Language: | English |
| OAI Identifier: | oai:ebuah.uah.es:10017/64500 |
| Online Access: | http://hdl.handle.net/10017/64500 https://dx.doi.org/10.1109/LPT.2019.2940297 |
| Access Level: | Open access |
| Keyword: | Distributed acoustic sensing Phase-OTDR Perturbation detection Electrónica Electronics |
| Summary: | 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. |
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