Commercial radar sensing is gaining relevance and machine learning algorithms constitute one of the key components that are enabling the spread of this radio technology into areas like surveillance or healthcare. However, radar datasets are still scarce and generalization cannot be yet achieved for all radar systems, environment conditions or design parameters. A certain degree of fine tuning is, therefore, usually required to deploy machine-learning-enabled radar applications. In this work, we consider the problem of unsupervised domain adaptation across radar configurations in the context of deep-learning human activity classification using frequency-modulated continuous-wave. For that, we focus on the theory-inspired technique of Margin Disparity Discrepancy, which has already been proved successful in the area of computer vision. Our experiments extend this technique to radar data, achieving a comparable accuracy to fewshot supervised approaches for the same classification problem.
翻译:商业雷达遥感越来越具有相关性,机器学习算法是使这种无线电技术能够传播到监视或保健等领域的关键组成部分之一,然而,雷达数据集仍然稀少,尚未对所有雷达系统、环境条件或设计参数进行概括化,因此,通常需要进行某种程度的微调,以部署机学辅助雷达应用。在这项工作中,我们考虑到在利用频率调控连续波进行深入学习的人类活动分类时,在雷达配置中进行不受监督的域适应的问题。为此,我们侧重于在计算机视觉领域已经证明成功的理论激发的玛格因分异异异异异性技术。我们的实验将这一技术扩大到雷达数据,在相同的分类问题上实现与少数受监督的方法相似的精确度。