## 1 Introduction  
Ultra-wideband (UWB) radar refers to a radar system in which the bandwidth of the transmitted signal is 25% greater than the central frequency. Compared to other sensors (such as acoustic and infrared sensors), UWB radar protects the privacy and can work normally in darkness. UWB radar is widely used in ground-penetrating radar (GPR), through-wall imaging, post-earthquake rescue, and so on, due to its high range resolution and high penetration capability [1; 2; 3; 4; 5; 6; 7]. The frequently benefitted waveforms in UWB radar literature are short impulses [8; 9; 10; 11], pseudo-random coded signals [12; 13; 14; 15], frequency-modulated continuous wave (FMCW) signals [16; 17; 18; 19; 20], and stepped-frequency continuous wave (SFCW) signals [21; 22; 23; 24]. UWB impulse radar has been increasingly adopted by researchers for human target detection due to its simple structure, low-cost, and low power consumption [25; 26; 27]. Considering the stationary human targets detection, periodic motions generated by human respiration or heartbeat can be utilized. However, in practical applications, the energy scattered by the heart is too weak to be detected due to the propagation attenuation of the body. For electromagnetic waves with a frequency of 300-900 MHz, the electromagnetic energy scattered by dry skin is approximately 72% of the incident energy [28]. UWB impulse radar can detect human targets mainly based on the chest motion caused by human respiration. Over the past two decades, many scholars have conducted research on the detection of human targets behind obstacles or walls. [PERSON] et al. [29] proposed a general respiratory response model by considering the human target as a moving scatterer whose range varies periodically. Based on this model, the methods such as mean subtraction (MS) [29], range profile subtraction (RPS) [30], and linear-trend subtraction (LTS) [31] have been proposed to suppress the stationary background signal. The respiration detection algorithm was complemented with singular value decomposition (SVD) by [PERSON] et al. [31]. On the one hand, the respiration signal can be largely separated from the non-stationary clutter, and on the other hand, the effect of the additive white Gaussian noise (AWGN) can be reduced. Higher order cumulant were used to suppress AWGN based on that higher order cumulant of any Gaussian noise is zero [32]. [PERSON] et al. [33] proposed a correlation-based method to enhance the weak respiration signal based on the relativity and periodicity of the signals. However, the above studies are all focused on the detection of weak respiration signal by developing algorithms to suppress background, noise, or enhance respiration signal. The automatic identification and detection of respiration signal is rarely studied by scholars. [PERSON] et al. [34] proposed a method based on the 1D range profile to automatically identify respiration response. They regard the energy peak as the detection unit and then utilize the correlation to distinguish the respiration signal from the clutter signal. [PERSON] et al. [35] proposed a method based on the 2D range-frequency matrix to automatically detect the human target. They design the constant false alarm ratio (CFAR) energy windows based on the characteristic of life sign. [PERSON] et al. [36] proposed a range-dependent thresholding which decreases exponentially and hard thresholding is applied to the range profile. However, these three methods are energy-based automatic detection methods, and there will be missed alarms in the case of multiple human targets. First, the energy scattered by the farther target is much lower than the energy scattered by the nearer target, resulting in the farther target being missed. Second, the frequency and intensity of the target's respiratory motion will also affect the intensity of the electromagnetic wave energy scattered by the target. Finally, in the case of multiple human targets, there is a shadow effect.