Detection of Multiple Stationary Humans Using UWB MIMO Radar

• Published in: Sensors (Basel, Switzerland) Vol. 16; no. 11; p. 1922
• Main Authors: Liang, Fulai, Qi, Fugui, An, Qiang, Lv, Hao, Chen, Fuming, Li, Zhao, Wang, Jianqi
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 16.11.2016; MDPI
• Subjects: Algorithms; Antennas; detection; Human subjects; Humans; Localization; multiple-input and multiple-output (MIMO); Radar; Radar systems; Sensors; Signal processing; Signal to noise ratio; ultra-wideband (UWB); vital sign; Vital Signs - physiology; multiple-input and multiple-output (MIMO); detection; radar; ultra-wideband (UWB); vital sign
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s16111922

Remarkable progress has been achieved in the detection of single stationary human. However, restricted by the mutual interference of multiple humans (e.g., strong sidelobes of the torsos and the shadow effect), detection and localization of the multiple stationary humans remains a huge challenge. In this paper, ultra-wideband (UWB) multiple-input and multiple-output (MIMO) radar is exploited to improve the detection performance of multiple stationary humans for its multiple sight angles and high-resolution two-dimensional imaging capacity. A signal model of the vital sign considering both bi-static angles and attitude angle of the human body is firstly developed, and then a novel detection method is proposed to detect and localize multiple stationary humans. In this method, preprocessing is firstly implemented to improve the signal-to-noise ratio (SNR) of the vital signs, and then a vital-sign-enhanced imaging algorithm is presented to suppress the environmental clutters and mutual affection of multiple humans. Finally, an automatic detection algorithm including constant false alarm rate (CFAR), morphological filtering and clustering is implemented to improve the detection performance of weak human targets affected by heavy clutters and shadow effect. The simulation and experimental results show that the proposed method can get a high-quality image of multiple humans and we can use it to discriminate and localize multiple adjacent human targets behind brick walls.