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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Unsupervised Adversarial Domain Adaptation for Micro-Doppler Based Human Activity Classification
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Unsupervised Adversarial Domain Adaptation for Micro-Doppler Based Human Activity Classification

机译：基于微多普勒的人类活动分类的无监督的对抗域适应

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摘要

The fundamental difficulties in the supervised deep learning algorithm are obtaining large-scale labeled data and generalizing the trained model to a new environment. In this letter, we propose an unsupervised domain adaption method for human activity classification using micro-Doppler signatures. We study on how to classify micro-Doppler signatures in a new domain using only labeled samples from a different domain, mainly focus on simulation-to-real-world deep domain adaptation. First, we use motion capture (MOCAP) database to generate simulated micro-Doppler data to train the convolutional neural network (CNN). Then, considering the difference between simulation and real-world domain distributions, we introduce a domain discriminator to pit against the feature extractor part of the CNN. Through this adversarial process, like the generative adversarial network, the CNN trained on the simulation domain is able to generalize to the real-world domain. Experiment results show that the proposed method achieves over 84.02 accuracy in real-world micro-Doppler classification, which outperforms nearly 16 in CNN trained on the annotated simulation without domain adaptation and performs better than the existing domain adaptation methods.
机译：监督深度学习算法中的根本困难是获得大规模标记数据并将培训的模型推广到新环境。在这封信中，我们提出了一种使用微多普勒签名的人类活动分类的无监督域适应方法。我们研究如何使用不同域的标记样本在新域中对微多普勒签名进行分类，主要关注仿真 - 真实的深层域适应。首先，我们使用运动捕获（Mocap）数据库来生成模拟的微多普勒数据以训练卷积神经网络（CNN）。然后，考虑模拟和真实世界域分布之间的差异，我们将域鉴别器引入挖掘CNN的特征提取器部分。通过这种对抗过程，如生成的对抗网络，在模拟域上训练的CNN能够概括到真实世界领域。实验结果表明，该方法在现实世界微多普勒分类中实现了超过84.02的精度，这在没有域适应的注释仿真上训练的CNN训练，并且比现有的域适应方法更优于达到的CNN训练。

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