## 1 Introduction  
In recent years, the application of ultrawideband (UWB) radar for remote sensing has emerged as a promising technique for the detection of human targets [1, 2, 3]. Unlike thermal imaging, X-rays, magnetic induction, and other technologies, UWB radar probes use nonionizing electromagnetic pulse waves, which are proven to be stable and immune to the influence of temperature, nonmetallic wall materials, and target clothes. The signals of the radiated pulse waves occupy an extremely broad bandwidth relative to the center frequency. Thus, UWB radars are beneficial for penetrating obstacles such as walls and separating multiple human targets in complex environment detection settings [4]. Moreover, UWB radars can locate human targets with higher accuracy compared with continuous-wave (CW) radar systems. Accordingly, the UWB radar has gradually become one of the most reliable technical means for detection and localization purposes during human postdisaster rescue missions, such as earthquakes and collapses [5, 6]. Radar signals reflected from human targets carry biometric information related to periodic fluctuations of the body during respiration [7]. For example, the periodic frequency of respiration of a healthy adult is in the range of 0.2-0.4 Hz. However, human target detection through ruins using UWB radars has several challenges, including the low reflectivity of human beings, strong interference from stationary objects, and high attenuation of electromagnetic waves, which could lead to an extremely low signal-to-clutter ratio (SCR) [8, 9]. In particular, several construction machineries are used to aid rescue efforts in modern postdisaster emergency rescues. Obviously, the vibration amplitude of mechanical operations is considerably larger than that of periodic motions caused by human respiration. Therefore, the signal-to-interference ratio (SIR) of the echo data would decrease considerably, and strong vibration interference would be sufficiently large to trigger false alarms. In recent years, several human detection techniques have been developed in the presence of interference, including time- and frequency-domain methods. Regarding the time-domain methods, [PERSON] et al. [10] proposed an adaptive filtering algorithm to suppress the vibration interference caused by a machinery operator's respiration using dual-frequency radar at 5.75 GHz/35 GHz for human detection. Accordingly, ground experiments showed that the proposed method can identify two types of respiratory signals with a frequency difference > 0.04 Hz. In the frequency domain, [PERSON] et al. [11] used a phase detection technology based on frequency differences to eliminate the inertial vibration interference of a driver's body caused by vehicle-induced vibration during driving. Therefore, it can accurately detect human breathing and heartbeat signals. Moreover, [PERSON] et al. [12] used acceleration sensors to acquire vehicular seat vibration signals, which were assumed to be in the same direction as the human body vibration. For cases of known direction of motion and acceleration, a method was proposed to separate human body vibration signals and physiological signals based on independent component analysis (ICA). [PERSON] et al. [13] segmented signals that contained vital signals and various human motions into subsignals and then constructed the desired vital signal using the correlation method. However, there is a relatively large frequency difference between the induced vibration interference and human respiration in the aforementioned methods. There is no guarantee that they are effective when the vibration interference and human respiration are in a similar frequency band. In our pre-experiments, it was found that parts of the mechanical vibration signals of wheel loaders were approximately quasiperiodic in the time domain, and their frequencies were in the range of human respiratory signal frequencies (0.2-0.4 Hz) with apparently larger amplitudes.