Work

A Deep-Learning Method for Radar Micro-Doppler Spectrogram Restoration

Year: 2020

Type: article

Abstract: 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.

Source: Sensors

DOI: 10.3390/s20175007

Authors: Yuan He, Xinyu Li, Runlong Li, Jianping Wang, Xiaojun Jing

Institutions: Beijing University of Posts and Telecommunications, Delft University of Technology

Language: English

Topic: Advanced SAR Imaging Techniques

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,112

Funders: National Natural Science Foundation of China, Beijing University of Posts and Telecommunications