Detecting Tunnels for Border Security based on Fiber Optical Distributed Acoustic Sensor Data using DBSCAN

Suleyman Aslangul

2020

Abstract

The Border Situational Awareness may consist of many different features. Mainly, these features focus on detecting intrusion activities. New generation security systems are collecting important amount of data obtained from sensors. In general, the alarm confirmation mechanism is visual identification using cameras and Video Management Systems. On the other hand, this approach may not be enough to identify an invisible tunnel digging activity underground for trespassing the border. This paper is suggesting a new method to detect tunnels by using statically filtered alarm data and DBSCAN algorithm. In this particular case MIDAS® Fiber Optic based Distributed Acoustic Sensor (DAS) system is used, which is designed by ASELSAN Inc. The proposed approach is evaluated and positive results are seen on diverse areas of the Turkish borders.

Paper Citation

in Harvard Style

Aslangul S. (2020). Detecting Tunnels for Border Security based on Fiber Optical Distributed Acoustic Sensor Data using DBSCAN. In Proceedings of the 9th International Conference on Sensor Networks - Volume 1: SENSORNETS, ISBN 978-989-758-403-9, pages 78-84. DOI: 10.5220/0008869600780084

in Bibtex Style

@conference{sensornets20,
author={Suleyman Aslangul},
title={Detecting Tunnels for Border Security based on Fiber Optical Distributed Acoustic Sensor Data using DBSCAN},
booktitle={Proceedings of the 9th International Conference on Sensor Networks - Volume 1: SENSORNETS,},
year={2020},
pages={78-84},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008869600780084},
isbn={978-989-758-403-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Sensor Networks - Volume 1: SENSORNETS,
TI - Detecting Tunnels for Border Security based on Fiber Optical Distributed Acoustic Sensor Data using DBSCAN
SN - 978-989-758-403-9
AU - Aslangul S.
PY - 2020
SP - 78
EP - 84
DO - 10.5220/0008869600780084