# Detection of Quasi-Static Trapped Human Being Using Mono-Static UWB Life-Detection Radar
## 1 Introduction  
The ultra-wideband (UWB) life-detection radar is widely used to indicate human beings trapped in obstacles in rescue applications [1; 2; 3; 4; 5; 6]. However, indicating the trapped human beings from raw radar echoes is extremely difficult due to heavy environment clutter, high signal attenuation of the obstacles, non-periodic micro-motion and low reflectivity of the human body [7; 8; 9; 10]. The existing vital sign detection algorithms are divided into two categories: fast Fourier transform (FFT)-based methods [11; 12; 13; 14] and constant false alarm rate (CFAR)-based methods. The former mainly aims at detecting the quasi-periodic breathing signal, while the latter mainly aims at tracking the large-scale human movements. However, under normal circumstances, the behaviors of the human body also include non-periodic, small-amplitude movements, such as body shaking back and forth, and non-periodic breathing, causing the characteristics of the vital signs between quasi-static breathing and large-scale movements. Therefore, the FFT-based method often cannot accurately determine the target position, and the CFAR-based method is difficult to distinguish the target from the background. Generally, quasi-/non-periodic respiration frequency or weak respiration amplitude is considered as a quasi-static vital sign for the trapped human beings. Nowadays, the detection of non-periodic but non-weak micro-motion and the detection of quasi-periodic but weak micro-motion are the research hotspots and difficulties in the field of life detection. Detection of the quasi-static trapped human beings is widely needed and urgent in public security applications. Recently, a large amount of research work on the detection strategies and radar systems in the field of vital signs detection has been reported. Table 1 lists the main parameters and characteristics of radars published in commercial and academic papers. The novelradar transceiver signal designs are proposed to obtain the higher signal-to-noise ratio (SNR) [15; 16; 17]. In through-the-wall scenes, the maximum detection distances of the doppler radar proposed in Reference [15], the frequency modulated continuous wave (FMCW) radar proposed in Reference [17] and the pseudo-random coded radar are 10, 10 and 17 m, respectively. The multiple input multiple output (MIMO)-based method uses a real aperture of multiple transceiver combinations, realizing instant imaging resolution [18; 19; 20; 21; 22], which is widely used in through-wall imaging. The multi-view and multi-station radar systems proposed in References [23; 24; 25; 26] can improve the efficiency of rescue in low SNR scenes by combining radar data in multiple observation points. In Reference [24], the proposed dual-station radar system provides a practical integrated approach for detecting and localizing stationary human targets. However, the improvement of detection capability through the optimization of transceiver signal is limited for the mono-static radar. Meanwhile, the balance between the radar sensitivity and radar false alarm should be considered in the system design. On the other hand, due to a large number of antennas in the MIMO system, the system is complex and bulky, which is not convenient for the practical field operations. Studies show that the novel multi-view system can achieve high detection capabilities and strong scene adaptability simultaneously. To sum up, most of the existing studies are optimized in a single aspect, such as increasing the detection range, realizing radar imaging and distinguishing between obscured targets, etc. In addition, the research on the signal detection of non-periodic but non-weak micro-motion is still an open question. So, the development of an UWB radar vital sign detection system that can flexibly perform multi-scene detection is becoming a priority, motivating the design of innovative radar systems and detection strategies in a combined way, increasing the rescue efficiency.