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Classification of Human Motions Using Micro-Doppler Radar in the Environments with Micro-Motion Interference.
- Source :
-
Sensors (14248220) . Jun2019, Vol. 19 Issue 11, p2598. 1p. - Publication Year :
- 2019
-
Abstract
- Human motion classification based on micro-Doppler effect has been widely used in various fields. However, the motion classification performance would be greatly degraded if the wireless environment has non-target micro-motion interference. In this case, the interference signal aliases with the signal of target human motions and then generates cross-terms, making the signals hard to be used to identify target human motions. Existing methods do not consider this non-target micro-motion interference and have poor resistance to such interference. In this paper, we propose a target human motion classification system that can work in the scenarios with non-target micro-motion interference. Specifically, we build a continuous wave radar transceiver working in a low-frequency radar band using the software defined radio equipment Universal Software Radio Peripheral (USRP) N210 to collect signals. Moreover, we use Empirical Mode Decomposition and S-transform successively to remove non-target micro-motion interference and improve the time-frequency resolution of the raw signal. Then, an Energy Aggregation method based on S-method is proposed, which can suppress cross-terms and background noise. Furthermore, we extract a set of features and classify four human motions by adopting Bagged Trees. Extensive experiments using the test-bed show that under the scenarios with non-target micro-motion interference, 97.3% classification accuracy can be achieved. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 19
- Issue :
- 11
- Database :
- Academic Search Index
- Journal :
- Sensors (14248220)
- Publication Type :
- Academic Journal
- Accession number :
- 137272478
- Full Text :
- https://doi.org/10.3390/s19112598