## 1 Introduction  
Natural disasters occur throughout the world and result in significant damage and loss of life. The detection of humans trapped in buildings and rubble in these situations is a significant challenge. As a result, non-contact detection of vital signs (VS) has received considerable attention in the literature [1; 2; 3; 4; 5]. Electromagnetic detection techniques are the most commonly employed. Ultra-wideband (UWB) radar has been used to track moving targets and for through-wall imaging because of the good permeability and excellent resolution [6; 7; 8; 9; 10; 11; 12; 13]. As a result, it has been proposed for vital sign detection [14; 15; 16; 17; 18; 19; 20; 21; 22] as shown in Figure 1. In [14], respiration-like clutter was suppressed using an adaptive cancellation method. A short-time Fourier Transform (STFT) was used for VS detection in [15]. However, these methods cannot accurately estimate the frequencies of VS signals due to the presence of harmonics. Further, STFT performance is sensitive to the window length. Ensembleempirical mode decomposition (EEMD) and a frequency window were used to remove clutter and harmonics in [17], but this increases the receiver complexity. An adaptive Kalman filter was developed to extract respiration signals from UWB radar data in [19]. An extended complex signal demodulation (CSD) technique was considered in [20] to eliminate the wrapped problem with the DACM algorithm. To avoid the codomain restriction in arctangent demodulation (AD), an extended differentiate and cross-multiply (DACM) method was proposed in [21]. Existing vital sign detection techniques are not suitable for trapped victims because they consider only some of the issues, such as removing clutter, determining respiration characteristics, and heart rate estimation [23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41]. An algorithm for vital sign detection based on radar data was developed in [16]. However, this approach is complex and frequently produces false-positive results. Figure 1: The model for vital sign detection using an UWB radar. The fast Fourier transform (FFT) and Hilbert-Huang transform (HHT) have been used to analyse respiration signals [23; 24]. Adaptive clutter cancellation has been proposed to remove respiration-like clutter [26]. The respiration period was estimated in [28] for an additive white Gaussian noise (AWGN) environment [28]. Respiration detection in low signal to noise and clutter ratio (SNCR) conditions was considered in [29] using singular value decomposition (SVD). The method proposed in [34] to detect signs of life is only effective over short distances and in high signal to noise ratio (SNR) environments. The effects of static clutter have been mitigated using linear trend subtraction (LTS) [35]. A higher order cumulant (HOC) method was used in [37] to extract vital sign information by exploiting the fact that the HOC of Gaussian noise is zero. Further, it was shown that this approach can be used for vital sign detection in other types of noise. Ensemble empirical mode decomposition was used in [38] for heart rate detection. Accurate acquisition of vital sign information such as the respiration frequency is difficult in many environments and existing approaches are complex. Further, they are effective only over short distances and require a high SNCR. Thus, in this paper, a new method is proposed to accurately detect vital signs in low SNCR environments such as long range and through-wall conditions. An automatic gain method is developed to improve the SNR. The kurtosis of the received signal is used in conjunction with a windowed Fourier transform (WFT) to enhance the performance. Further, a frequency accumulator is employed to obtain both the range and respiration frequency. The performance of the proposed system is verified experimentally with real test subjects using the UWB radar system designed by the Chinese Academy of Sciences. The remainder of this paper is organized as follows.