Continuous Human Motion Recognition Using Micro-Doppler Signatures in the Scenario With Micro Motion Interference

• Published in: IEEE sensors journal Vol. 21; no. 4; pp. 5022 - 5034
• Main Authors: Zhao, Running, Ma, Xiaolin, Liu, Xinhua, Li, Fangmin
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.02.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Continuous human motion recognition; deep learning; Feature extraction; Frequency distribution; Human motion; Indexes; Interference; micro-Doppler; Motion perception; non-target micro motion interference; Radar; Recognition; Sensors; Signatures; Time-frequency analysis; Torso
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2020.3033278

The application of micro-Doppler-based continuous human motion recognition (HMR) is greatly hindered by non-target micro motion interference, due to the deformation of micro-Doppler signatures of target human motion caused by such interference. In this paper, we propose a novel continuous HMR method using micro-Doppler signatures that can work in the scenario with non-target micro motion interference. Specifically, a signal preprocessing architecture is designed, where the empirical mode decomposition is employed to remove the interference in radar raw signal and the multiwindow time-frequency representation is used to generate the time-frequency distribution (TFD) with high concentration. Moreover, a tailored network, that integrates multiscale squeeze-and-excitation network for feature sequence extraction, stacked bidirectional long short-term memory for sequence labeling and connectionist temporal classification algorithm for label transcription, is employed to recognize continuous human motion from TFD. The experimental results show that the proposed method outperforms existing methods in terms of recognition accuracy and generalization.