Multitarget Respiration Detection With Adaptive Digital Beamforming Technique Based on SIMO Radar

The Doppler radar has been widely used in respiration detection. However, most of the existing microwave respiration detection works are intended for either a single human subject in front of the radar or multiple subjects with known positions. In this article, a system based on single-input-multipl...

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Bibliographic Details
Published inIEEE transactions on microwave theory and techniques Vol. 68; no. 11; pp. 4814 - 4824
Main Authors Xiong, Junjun, Hong, Hong, Zhang, Hongqiang, Wang, Ning, Chu, Hui, Zhu, Xiaohua
Format Journal Article
LanguageEnglish
Published New York IEEE 01.11.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Adaptive digital beamforming (ADBF)
Adaptive systems
Antenna arrays
Array signal processing
Beamforming
Continuous radiation
Direction of arrival
direction-of-arrival (DOA) estimation
Doppler radar
Human subjects
multiple targets
Radar antennas
Radar detection
Receiving antennas
Respiration
respiration detection
single-input multiple-output (SIMO)
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Summary:The Doppler radar has been widely used in respiration detection. However, most of the existing microwave respiration detection works are intended for either a single human subject in front of the radar or multiple subjects with known positions. In this article, a system based on single-input-multiple-output (SIMO) continuous-wave (CW) radar equipped with adaptive digital beamforming (ADBF) technique is presented to detect the respiration of multiple human subjects at unknown positions simultaneously. A solution based on the modified Capon (m-Capon) direction-of-arrival (DOA) estimation and linear constraint minimal variance (LCMV) ADBF is proposed to automatically find the angles of human subjects. By forming spatially distributed beams toward the subjects of interest, human respiration can be remotely obtained. Furthermore, for the detection of each person's respiration, nulls are also generated at the angles of nearby interfering subjects, which results in high target discrimination capability when multiple human subjects are close to each other. The experimental results show good detection accuracy compared with the reference sensor, which verifies the effectiveness of the developed system for multitarget respiration detection.
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content type line 14
ISSN:0018-9480
1557-9670
DOI:10.1109/TMTT.2020.3020082