With the application of deep learning (DL) techniques, radar-based human activity recognition (HAR) attracts increasing attention thanks to its high accuracy and good privacy. However, training a DL model requires a large volume of data, and generally the trained model cannot be adapted to a new scenario. In this paper, we propose a supervised few-shot adversarial domain adaptation ( FS-ADA ) method for HAR, where only limited radar training data is collected from a new application scenario. We adopt the domain adaptation method to learn a common feature space between a pre-existing radar dataset and the newly acquired training data. We also design a multi-class discriminator network, which integrates the category classifier and the binary domain discriminator, to employ the supervised label information in the limited radar data for model training. Then, a multitask generative adversarial training mechanism is proposed to optimize FS-ADA . In this way, both domain-invariant and category-discriminative features can be extracted for HAR in a new scenario. Experimental results for two few-shot radar-based HAR tasks show that the proposed FS-ADA method is effective and outperforms state-of-the-art methods.

中文翻译：

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基于少镜头雷达的人类活动识别的监督域适应

随着深度学习（DL）技术的应用，基于雷达的人体活动识别（HAR）由于其高精度和良好的隐私性而受到越来越多的关注。但是，训练一个深度学习模型需要大量的数据，而且训练出来的模型一般无法适应新的场景。在本文中，我们提出了一种有监督的小样本对抗域适应（ FS-ADA ) 方法用于 HAR，其中仅从新应用场景中收集有限的雷达训练数据。我们采用域自适应方法来学习预先存在的雷达数据集和新获得的训练数据之间的公共特征空间。我们还设计了一个多类鉴别器网络，它集成了类别分类器和二元域鉴别器，利用有限雷达数据中的监督标签信息进行模型训练。然后，提出了一种多任务生成对抗训练机制来优化FS-ADA。通过这种方式，可以在新场景中为 HAR 提取域不变和类别区分特征。两个基于少量雷达的 HAR 任务的实验结果表明，所提出的FS-ADA 方法是有效的，并且优于最先进的方法。