| Paper ID | ASPS-4.6 |
| Paper Title |
ADAPTIVE RF FINGERPRINT DECOMPOSITION IN MICRO UAV DETECTION BASED ON MACHINE LEARNING |
| Authors |
Chengtao Xu, Fengyu He, Bowen Chen, Yushan Jiang, Houbing Song, Embry-Riddle Aeronautical University, United States |
| Session | ASPS-4: Autonomous Systems |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 13:00 - 13:45 |
| Presentation Time: | Thursday, 10 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Applied Signal Processing Systems: Signal Processing Systems [DIS-EMSA] |
| IEEE Xplore Open Preview |
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| Virtual Presentation |
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| Abstract |
Radio frequency (RF) signal classification has significantly been used for detecting and identifying the features of unknown unmanned aerial vehicles (UAVs). This paper proposes a method using empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) on extracting the communication channel characteristics of intruding UAVs. The decomposed intrinsic mode functions (IMFs) except noise components are selected for RF signal pattern recognition based on machine learning (ML). The classification results show that the denoising effects introduced by EMD and EEMD could both fit in improving the detection accuracy with different features of RF communication channel, especially on identifying time-varying RF signal sources. |
