SNR-dependent drone classification using convolutional neural networks
SNR-dependent drone classification using convolutional neural networks
复制标题
使用卷积神经网络进行依赖于信噪比的无人机分类
DOI：
10.1049/rsn2.12161
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Dale H
中科院分区：
文献类型：
--
作者：
Dale H
雷达传感提供了一种实现 24 小时全天候无人机监控的方法，但为了发挥最大效果，系统需要能够区分鸟类和无人机。这项工作检查了无人机-鸟类分类性能与信噪比 (SNR) 的函数关系。为了对雷达截面 (RCS) 较小的无人机进行分类，以及促进在较远距离上进行可靠分类，必须在低 SNR 值下进行分类。为了研究分类性能和 SNR 之间的关系，将高斯噪声添加到通过实验获得的雷达频谱图数据集中。分类由卷积神经网络 (CNN) 执行。结果表明，对于可用数据，分类精度随着 SNR 的下降而下降，正如任何给定 CNN 所预期的那样。给出了性能随信噪比降低而降低的程度。进一步表明，更简单的网络架构对噪声的鲁棒性更强。最后，证明数据增强可以用作在较低 SNR 值下提高分类精度的方法。贝叶斯优化用于寻找最佳增强超参数，总体而言，在低 SNR 下实现了 92% 的分类精度。
Radar sensing offers a method of achieving 24‐h all‐weather drone surveillance, but in order to be maximally effective, systems need to be able to discriminate between birds and drones. This work examines drone‐bird classification performance as a function of signal to noise ratio (SNR). Classification at low SNR values is necessary in order to classify drones with a small radar cross‐section (RCS), as well as to facilitate reliable classification at longer ranges. To investigate the relationship between classification performance and SNR, Gaussian noise is added to an experimentally obtained dataset of radar spectrograms. Classification is performed by convolutional neural networks (CNNs). It is shown that for the data available classification accuracy drops with falling SNR, as might be expected for any given CNN. The degree to which performance degrades with reduced SNR is presented. It is further shown that simpler network architectures are more robust to noise. Finally, it is demonstrated that data augmentation can be used as a means of enhancing classification accuracy at lower SNR values. Bayesian optimisation is used to find the optimal augmentation hyperparameters and overall, classification accuracies of 92% are achieved at low SNR.