| Title | Adaptive RF Fingerprint Decomposition in Micro UAV Detection based on Machine Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Xu, Chengtao, He, Fengyu, Chen, Bowen, Jiang, Yushan, Song, Houbing |
| Conference Name | ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
| Date Published | jun |
| Keywords | Communication channels, composability, compositionality, decomposition, EEMD, EMD, Empirical mode decomposition, feature extraction, Fingerprint recognition, machine learning, Metrics, Micro UAS Detection, pubcrawl, Radio frequency, RF Fingerprint, RF signals, time-varying RF signal |
| 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. |
| DOI | 10.1109/ICASSP39728.2021.9414985 |
| Citation Key | xu_adaptive_2021 |
Adaptive RF Fingerprint Decomposition in Micro UAV Detection based on Machine Learning