Noisy Drone RF Signal Classification v2

We provide a preprocessed dataset to enable model development for the detection/classification of drone RF signals. It consists of the non-overlapping signal vectors of length of 1048576 samples, which corresponds to approx. 74.9ms at 14MHz. We have also added Labnoise (Bluetooth, Wi-Fi, Amplifier) and Gaussian noise to the dataset.

After normalization, the drone signals were mixed with either Labnoise (50%) or Gaussian noise (50%). The noise class was created by mixing Labnoise and Gaussian noise in all possible combinations (i.e., Labnoise + Labnoise, Labnoise + Gaussian noise, Gaussian noise + Labnoise, and Gaussian noise + Gaussian noise). For the drone signal classes, as for the noise class, the number of samples for each level of SNR is equally distributed over the interval of SNR in [-20, 30]dB in steps of 2dB, i.e., 679 - 685 samples per SNR. The resulting number of samples per class is shown in the Table below.

DJI | FutabaT14 | FutabaT7 | Graupner | Taranis | Turnigy | Noise|
|---|---|---|---|---|---|---|
| 1280 | 3472 |801 | 801 | 1663 | 855 | 8872 |

#Code:
See https://github.com/sgluege/Robust-Drone-Detection-and-Classification for a script to load and inspect the dataset. Further you'll find code to train and evaluate a model.

#Related Literature:
Further information about the data, and how to build a classifier, can be found in our related manuscript. Please cite it if you find it useful.

S. Glüge, M. Nyfeler, A. Aghaebrahimian, N. Ramagnano and C. Schüpbach, "Robust Low-Cost Drone Detection and Classification Using Convolutional Neural Networks in Low SNR Environments," in IEEE Journal of Radio Frequency Identification, vol. 8, pp. 821-830, 2024, doi: 10.1109/JRFID.2024.3487303

Bibtex:
```
@ARTICLE{10737118,
author={Glüge, Stefan and Nyfeler, Matthias and Aghaebrahimian, Ahmad and Ramagnano, Nicola and Schüpbach, Christof},
journal={IEEE Journal of Radio Frequency Identification},
title={Robust Low-Cost Drone Detection and Classification Using Convolutional Neural Networks in Low SNR Environments},
year={2024},
volume={8},
number={},
pages={821-830},
doi={10.1109/JRFID.2024.3487303}
}
```

本数据集旨在支持无人机射频信号的检测与分类模型的开发。数据集包含长度为1048576样本的非重叠信号向量，相当于14MHz下约74.9毫秒的信号。此外，我们还向数据集中加入了实验室噪声（蓝牙、Wi-Fi、放大器）和高斯噪声。经过标准化处理后，无人机信号与实验室噪声（占比50%）或高斯噪声（占比50%）进行混合。噪声类别是通过将实验室噪声与高斯噪声以所有可能的组合方式（即实验室噪声+实验室噪声、实验室噪声+高斯噪声、高斯噪声+实验室噪声和高斯噪声+高斯噪声）混合而成的。对于无人机信号类别，与噪声类别相同，每个信噪比级别的样本数量在[-20, 30]dB的信噪比区间内均匀分布，即每2dB一个级别，每个信噪比级别的样本数量为679至685个。每个类别的样本数量详见下表。

| 品牌 | DJI | FutabaT14 | FutabaT7 | Graupner | Taranis | Turnigy | 噪声 |
|---|---|---|---|---|---|---|---|
| 样本数 | 1280 | 3472 | 801 | 801 | 1663 | 855 | 8872 |

#代码：
请参阅https://github.com/sgluege/Robust-Drone-Detection-and-Classification获取加载和检查数据集的脚本。此外，您还可以找到用于训练和评估模型的代码。

#相关文献：
关于数据集的详细信息以及如何构建分类器，可参考我们的相关研究论文。如发现此论文对您有所帮助，请予以引用。

S. Glüge, M. Nyfeler, A. Aghaebrahimian, N. Ramagnano 和 C. Schüpbach, "在低信噪比环境下使用卷积神经网络实现鲁棒且低成本的无人机检测与分类,