Unmanned aerial system detection and assessment through temporal frequency analysis

• Published in: 2017 International Carnahan Conference on Security Technology (ICCST) pp. 1 - 5
• Main Authors: Woo, Bryana L., Birch, Gabriel C., Stubbs, Jaclynn J., Kouhestani, Camron
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-10-2017
• Subjects: Algorithm design and analysis; Birds; Cameras; counter unmanned aerial systems; frequency analysis; machine learning; Machine learning algorithms; Phantoms; Security; temporal frequency signatures; Testing
• ISSN: 2153-0742
• DOI: 10.1109/CCST.2017.8167832

There is a desire to detect and assess unmanned aerial systems (UAS) with a high probability of detection and low nuisance alarm rates in numerous fields of security. Currently available solutions rely upon exploiting electronic signals emitted from the UAS. While these methods may enable some degree of security, they fail to address the emerging domain of autonomous UAS that do not transmit or receive information during the course of a mission. We examine frequency analysis of pixel fluctuation over time to exploit the temporal frequency signature present in imagery data of UAS. This signature is present for autonomous or controlled multirotor UAS and allows for lower pixels-on-target detection. The methodology also acts as a method of assessment due to the distinct frequency signatures of UAS when examined against the standard nuisance alarms such as birds or non-UAS electronic signal emitters. The temporal frequency analysis method is paired with machine learning algorithms to demonstrate a UAS detection and assessment method that requires minimal human interaction. The use of the machine learning algorithm allows each necessary human assess to increase the likelihood of autonomous assessment, allowing for increased system performance over time.