A5010154705- UAV Applications and Optimization
- Wireless Signal Modulation Classification
- Advanced SAR Imaging Techniques
- IoT-based Smart Home Systems
- Millimeter-Wave Propagation and Modeling
Xidian University
2023-2024
Recent studies have demonstrated that using deep-learning (DL) methods to classify drones based on the radio-frequency (RF) signal is effective. As known, rich and diverse data an important guarantee for good identification performance. In reality, due complexity high dynamic of wireless environments, costs collection labeling with sufficient diversity are often unacceptable. this work, we propose a low-cost augmentation (DA) method improve robustness neural network (NN). It generates extra...
Utilizing deep learning (DL) to identify drones through radio signals has been proven be a promising approach. However, two significant challenges remain solved. The first is how effectively at the low signal-to-noise ratio (SNR) regime, and second stably among numerous unknown interferences. In theory, sufficient data can alleviate above problems, but costs of signal acquisition labeling are usually unacceptable. this work, we aim improve robustness feature representation by introducing...
Radio frequency (RF)-based drone identification technologies have the advantages of long effective distances and low environmental dependence, which has become indispensable for surveillance systems. However, since drones operate in unlicensed bands, a large number co-frequency devices exist these brings great challenge to traditional signal methods. Deep learning techniques provide new approach complete end-to-end by directly distribution RF data. In such scenarios, due complexity high...