Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Beijing University of Posts and Telecommunications, Beijing 100876, China.
Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), Delft University of Technology, 2628CD Delft, The Netherlands.
Sensors (Basel). 2020 Sep 3;20(17):5007. doi: 10.3390/s20175007.
Radio frequency interference, which makes it difficult to produce high-quality radar spectrograms, is a major issue for micro-Doppler-based human activity recognition (HAR). In this paper, we propose a deep-learning-based method to detect and cut out the interference in spectrograms. Then, we restore the spectrograms in the cut-out region. First, a fully convolutional neural network (FCN) is employed to detect and remove the interference. Then, a coarse-to-fine generative adversarial network (GAN) is proposed to restore the part of the spectrogram that is affected by the interferences. The simulated motion capture (MOCAP) spectrograms and the measured radar spectrograms with interference are used to verify the proposed method. Experimental results from both qualitative and quantitative perspectives show that the proposed method can mitigate the interference and restore high-quality radar spectrograms. Furthermore, the comparison experiments also demonstrate the efficiency of the proposed approach.
射频干扰使得难以产生高质量的雷达频谱图，这是基于微多普勒的人体活动识别（HAR）的一个主要问题。在本文中，我们提出了一种基于深度学习的方法来检测和切除频谱图中的干扰。然后，我们恢复切除区域的频谱图。首先，我们使用全卷积神经网络（FCN）来检测和去除干扰。然后，我们提出了一种从粗到精的生成对抗网络（GAN）来恢复受干扰影响的频谱图部分。我们使用模拟运动捕捉（MOCAP）频谱图和带有干扰的实测雷达频谱图来验证所提出的方法。从定性和定量两个方面的实验结果表明，所提出的方法可以减轻干扰并恢复高质量的雷达频谱图。此外，对比实验也证明了所提出方法的有效性。
