The Effect of Real-World Interference on CNN Feature Extraction and Machine Learning Classification of Unmanned Aerial Systems
Small unmanned aerial systems (UASs) present many potential solutions and enhancements to industry today but equally pose a significant security challenge. We only need to look at the levels of disruption caused by UASs at airports in recent years. The accuracy of UAS detection and classification sy...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/8/7/179 |
| _version_ | 1797527875459481600 |
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| author | Carolyn J. Swinney John C. Woods |
| author_facet | Carolyn J. Swinney John C. Woods |
| author_sort | Carolyn J. Swinney |
| collection | DOAJ |
| description | Small unmanned aerial systems (UASs) present many potential solutions and enhancements to industry today but equally pose a significant security challenge. We only need to look at the levels of disruption caused by UASs at airports in recent years. The accuracy of UAS detection and classification systems based on radio frequency (RF) signals can be hindered by other interfering signals present in the same frequency band, such as Bluetooth and Wi-Fi devices. In this paper, we evaluate the effect of real-world interference from Bluetooth and Wi-Fi signals concurrently on convolutional neural network (CNN) feature extraction and machine learning classification of UASs. We assess multiple UASs that operate using different transmission systems: Wi-Fi, Lightbridge 2.0, OcuSync 1.0, OcuSync 2.0 and the recently released OcuSync 3.0. We consider 7 popular UASs, evaluating 2 class UAS detection, 8 class UAS type classification and 21 class UAS flight mode classification. Our results show that the process of CNN feature extraction using transfer learning and machine learning classification is fairly robust in the presence of real-world interference. We also show that UASs that are operating using the same transmission system can be distinguished. In the presence of interference from both Bluetooth and Wi-Fi signals, our results show 100% accuracy for UAV detection (2 classes), 98.1% (+/−0.4%) for UAV type classification (8 classes) and 95.4% (+/−0.3%) for UAV flight mode classification (21 classes). |
| first_indexed | 2024-03-10T09:49:02Z |
| format | Article |
| id | doaj.art-999bce1da57d43668b6e0c8d43f9b141 |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-10T09:49:02Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-999bce1da57d43668b6e0c8d43f9b1412023-11-22T02:56:11ZengMDPI AGAerospace2226-43102021-07-018717910.3390/aerospace8070179The Effect of Real-World Interference on CNN Feature Extraction and Machine Learning Classification of Unmanned Aerial SystemsCarolyn J. Swinney0John C. Woods1Computer Science and Electronic Engineering Department, University of Essex, Colchester CO4 3SQ, UKComputer Science and Electronic Engineering Department, University of Essex, Colchester CO4 3SQ, UKSmall unmanned aerial systems (UASs) present many potential solutions and enhancements to industry today but equally pose a significant security challenge. We only need to look at the levels of disruption caused by UASs at airports in recent years. The accuracy of UAS detection and classification systems based on radio frequency (RF) signals can be hindered by other interfering signals present in the same frequency band, such as Bluetooth and Wi-Fi devices. In this paper, we evaluate the effect of real-world interference from Bluetooth and Wi-Fi signals concurrently on convolutional neural network (CNN) feature extraction and machine learning classification of UASs. We assess multiple UASs that operate using different transmission systems: Wi-Fi, Lightbridge 2.0, OcuSync 1.0, OcuSync 2.0 and the recently released OcuSync 3.0. We consider 7 popular UASs, evaluating 2 class UAS detection, 8 class UAS type classification and 21 class UAS flight mode classification. Our results show that the process of CNN feature extraction using transfer learning and machine learning classification is fairly robust in the presence of real-world interference. We also show that UASs that are operating using the same transmission system can be distinguished. In the presence of interference from both Bluetooth and Wi-Fi signals, our results show 100% accuracy for UAV detection (2 classes), 98.1% (+/−0.4%) for UAV type classification (8 classes) and 95.4% (+/−0.3%) for UAV flight mode classification (21 classes).https://www.mdpi.com/2226-4310/8/7/179unmanned aerial vehiclesunmanned aerial systemsinterferenceUAS detectionRF spectrum analysismachine learning classification |
| spellingShingle | Carolyn J. Swinney John C. Woods The Effect of Real-World Interference on CNN Feature Extraction and Machine Learning Classification of Unmanned Aerial Systems Aerospace unmanned aerial vehicles unmanned aerial systems interference UAS detection RF spectrum analysis machine learning classification |
| title | The Effect of Real-World Interference on CNN Feature Extraction and Machine Learning Classification of Unmanned Aerial Systems |
| title_full | The Effect of Real-World Interference on CNN Feature Extraction and Machine Learning Classification of Unmanned Aerial Systems |
| title_fullStr | The Effect of Real-World Interference on CNN Feature Extraction and Machine Learning Classification of Unmanned Aerial Systems |
| title_full_unstemmed | The Effect of Real-World Interference on CNN Feature Extraction and Machine Learning Classification of Unmanned Aerial Systems |
| title_short | The Effect of Real-World Interference on CNN Feature Extraction and Machine Learning Classification of Unmanned Aerial Systems |
| title_sort | effect of real world interference on cnn feature extraction and machine learning classification of unmanned aerial systems |
| topic | unmanned aerial vehicles unmanned aerial systems interference UAS detection RF spectrum analysis machine learning classification |
| url | https://www.mdpi.com/2226-4310/8/7/179 |
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