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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 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 effectdoi:10.3390/aerospace8070179 doaj:999bce1da57d43668b6e0c8d43f9b141 fatcat:p63y2ggihbauldu6wxapsc4n24