Continuous Human Motion Recognition Using Micro-Doppler Signatures in the Scenario With Micro Motion Interference
| Autor: | Running Zhao, Xiaolin Ma, Fangmin Li, Xinhua Liu |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer science
business.industry 010401 analytical chemistry Feature extraction Pattern recognition 01 natural sciences Signal Sequence labeling Hilbert–Huang transform 0104 chemical sciences law.invention Interference (communication) law Feature (machine learning) Artificial intelligence Electrical and Electronic Engineering Radar business Instrumentation |
| Zdroj: | IEEE Sensors Journal. 21:5022-5034 |
| ISSN: | 2379-9153 1530-437X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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