RF-Based UAV Detection and Identification Using Hierarchical Learning Approach
Unmanned Aerial Vehicles (UAVs) are widely available in the current market to be used either for recreation as a hobby or to serve specific industrial requirements, such as agriculture and construction. However, illegitimate and criminal usage of UAVs is also on the rise which introduces their effec...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-03-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/6/1947 |
| _version_ | 1797541831795277824 |
|---|---|
| author | Ibrahim Nemer Tarek Sheltami Irfan Ahmad Ansar Ul-Haque Yasar Mohammad A. R. Abdeen |
| author_facet | Ibrahim Nemer Tarek Sheltami Irfan Ahmad Ansar Ul-Haque Yasar Mohammad A. R. Abdeen |
| author_sort | Ibrahim Nemer |
| collection | DOAJ |
| description | Unmanned Aerial Vehicles (UAVs) are widely available in the current market to be used either for recreation as a hobby or to serve specific industrial requirements, such as agriculture and construction. However, illegitimate and criminal usage of UAVs is also on the rise which introduces their effective identification and detection as a research challenge. This paper proposes a novel machine learning-based for efficient identification and detection of UAVs. Specifically, an improved UAV identification and detection approach is presented using an ensemble learning based on the hierarchical concept, along with pre-processing and feature extraction stages for the Radio Frequency (RF) data. Filtering is applied on the RF signals in the detection approach to improve the output. This approach consists of four classifiers and they are working in a hierarchical way. The sample will pass the first classifier to check the availability of the UAV, and then it will specify the type of the detected UAV using the second classifier. The last two classifiers will handle the sample that is related to Bebop and AR to specify their mode. Evaluation of the proposed approach with publicly available dataset demonstrates better efficiency compared to existing detection systems in the literature. It has the ability to investigate whether a UAV is flying within the area or not, and it can directly identify the type of UAV and then the flight mode of the detected UAV with accuracy around 99%. |
| first_indexed | 2024-03-10T13:21:14Z |
| format | Article |
| id | doaj.art-5ce6301eaeac4c1e9e0a501e7160c51b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T13:21:14Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5ce6301eaeac4c1e9e0a501e7160c51b2023-11-21T09:58:20ZengMDPI AGSensors1424-82202021-03-01216194710.3390/s21061947RF-Based UAV Detection and Identification Using Hierarchical Learning ApproachIbrahim Nemer0Tarek Sheltami1Irfan Ahmad2Ansar Ul-Haque Yasar3Mohammad A. R. Abdeen4Department of Computer Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi ArabiaDepartment of Computer Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi ArabiaDepartment of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi ArabiaTransportation Research Institute, Hasselt University, BE3500 Hasselt, BelgiumThe Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi ArabiaUnmanned Aerial Vehicles (UAVs) are widely available in the current market to be used either for recreation as a hobby or to serve specific industrial requirements, such as agriculture and construction. However, illegitimate and criminal usage of UAVs is also on the rise which introduces their effective identification and detection as a research challenge. This paper proposes a novel machine learning-based for efficient identification and detection of UAVs. Specifically, an improved UAV identification and detection approach is presented using an ensemble learning based on the hierarchical concept, along with pre-processing and feature extraction stages for the Radio Frequency (RF) data. Filtering is applied on the RF signals in the detection approach to improve the output. This approach consists of four classifiers and they are working in a hierarchical way. The sample will pass the first classifier to check the availability of the UAV, and then it will specify the type of the detected UAV using the second classifier. The last two classifiers will handle the sample that is related to Bebop and AR to specify their mode. Evaluation of the proposed approach with publicly available dataset demonstrates better efficiency compared to existing detection systems in the literature. It has the ability to investigate whether a UAV is flying within the area or not, and it can directly identify the type of UAV and then the flight mode of the detected UAV with accuracy around 99%.https://www.mdpi.com/1424-8220/21/6/1947radio frequencyunmanned aerial vehiclesmachine learningdetection and identification |
| spellingShingle | Ibrahim Nemer Tarek Sheltami Irfan Ahmad Ansar Ul-Haque Yasar Mohammad A. R. Abdeen RF-Based UAV Detection and Identification Using Hierarchical Learning Approach Sensors radio frequency unmanned aerial vehicles machine learning detection and identification |
| title | RF-Based UAV Detection and Identification Using Hierarchical Learning Approach |
| title_full | RF-Based UAV Detection and Identification Using Hierarchical Learning Approach |
| title_fullStr | RF-Based UAV Detection and Identification Using Hierarchical Learning Approach |
| title_full_unstemmed | RF-Based UAV Detection and Identification Using Hierarchical Learning Approach |
| title_short | RF-Based UAV Detection and Identification Using Hierarchical Learning Approach |
| title_sort | rf based uav detection and identification using hierarchical learning approach |
| topic | radio frequency unmanned aerial vehicles machine learning detection and identification |
| url | https://www.mdpi.com/1424-8220/21/6/1947 |
| work_keys_str_mv | AT ibrahimnemer rfbaseduavdetectionandidentificationusinghierarchicallearningapproach AT tareksheltami rfbaseduavdetectionandidentificationusinghierarchicallearningapproach AT irfanahmad rfbaseduavdetectionandidentificationusinghierarchicallearningapproach AT ansarulhaqueyasar rfbaseduavdetectionandidentificationusinghierarchicallearningapproach AT mohammadarabdeen rfbaseduavdetectionandidentificationusinghierarchicallearningapproach |