As far as low-cost deployment is concerned, wireless network-based device-free sensing (DFS) is of great interest and has successfully demonstrated the feasibility in Foliage penetration (FOPEN) target recognition. The classification accuracy of this technology is known to dramatically decrease in extreme climates where the received signals tend to be severely attenuated; while deep learning approaches have boosted performance, they only perform effectively when trained with large amounts of labeled data. Consequently, it is still unknown how to ensure reasonable detection accuracy in extreme climates where sufficient samples are difficult to obtain. To address this concern, we adopt two special measures for performance enhancement in this paper. One measure is to employ higher-order spectral (HOS) analysis to transform the time-domain signals into the bispectrum image representations, so that the shift to an image classification task could provide the advantage of using the existing Convolutional Neural Network (CNN) models. More importantly, the immunity of the approach against the unwanted clutters in foliage environments can be improved. The other one is to present an end-to-end Deep Learning Data Augmentation and Classification (DLDAC) model comprised of a Deep Convolutional Generative Adversarial Network (for data augmentation) and a SqueezeNet CNN backbone (for target classification), which can improve the classifier performance by using the augmented data on-the-fly. Thus, the negative impacts of low data regimes in extreme climates can be considerably accommodated. To evaluate the effectiveness of the proposed approach, comprehensive experiments are conducted on a real FOPEN dataset collected by impulse-radio ultra-wideband (IR-UWB) transceivers under three severe weather conditions. The experimental results demonstrate that even when only 300 training samples are taken for each type of target under every weather condition, the average classification accuracy of the proposed approach is still better than 92% in terms of distinguishing between human and other targets.

就低成本部署而言，基于无线网络的无设备感测（DFS）引起了人们极大的兴趣，并已成功证明了在叶子穿透（FOPEN）目标识别中的可行性。众所周知，在极端气候下，该技术的分类精度会大大降低，在极端气候下，接收到的信号往往会严重衰减。虽然深度学习方法提高了性能，但只有在训练有大量标记数据的情况下，它们才能有效发挥作用。因此，在难以获得足够样本的极端气候下，如何确保合理的检测精度仍然是未知的。为了解决这个问题，我们在本文中采取了两种特殊的措施来提高性能。一种措施是采用高阶频谱（HOS）分析将时域信号转换为双谱图像表示，以便转移到图像分类任务可以提供使用现有卷积神经网络（CNN）模型的优势。更重要的是，可以提高该方法对树叶环境中不需要的杂物的抵抗力。另一个是提出一种端到端深度学习数据增强和分类（DLDAC）模型，该模型由深度卷积生成对抗网络（用于数据增强）和SqueezeNet CNN主干（用于目标分类）组成，可以改善通过即时使用增强数据来实现分类器性能。因此，可以很好地适应低数据体制在极端气候下的负面影响。为了评估该方法的有效性，在三个恶劣天气条件下，对由脉冲无线电超宽带（IR-UWB）收发器收集的实际FOPEN数据集进行了全面的实验。实验结果表明，即使在每种天气条件下每种类型的目标仅采集300个训练样本，该方法在区分人为目标和其他目标方面的平均分类准确率仍优于92％。