## 1 Introduction
Radar technology facilitates robust human activity recognition in challenging environments, including those with high noise [54], aligning with research endeavors focused on the application of phase and frequency analysis for the identification of vital signs in high-noise settings. In particular, a frequency-shift keying radar in the 2.45-GHz band has been proposed for accurate vital sign detection [55]. Recent innovations in radar detection techniques, which integrate advanced signal processing and AI, have demonstrated significant improvements in tasks such as fall detection and activity classification [56]. Additionally, deep convolutional autoencoders have been employed for denoising in a near-field situation with moving objects using FMCW radar [57]. Doppler radars identify life signs for earthquake rescue with micro-Doppler signatures, using EMD signals as input to the binary classifier [38]. Contactless real-time heartbeat detection via 24 GHz continuous-wave Doppler radar using simple feedforward artificial neural networks (ANNs) has been achieved [58]. A diverse range of applications utilizing convolutional neural networks (CNNs) includes a movement detection system employing a continuous-wave Doppler radar sensor for detecting coughing and other gestures, multiple stationary human target detection in through-wall UWB radar, and real-time monitoringof vital signs based on impulse radio UWB radar during sleep [59; 60; 61]. A variety of deep learning (DL) techniques have been employed for non-contact physiological monitoring, including an efficient heart rate measurement system that utilizes medical radar and LSTM neural network, as well as a deep neural network (DNN) model capable of classifying respiratory signals into inhalation and exhalation using non-contact UWB radar [62; 63]. The real-world application of measuring earthquake rubble involves detecting human vital signs using ground penetrating radar, with future research focusing on signal processing techniques [64]. Additionally, spatiotemporal processing for the remote sensing of trapped victims using 4D imaging radar in real debris has not utilized AI-enabled methods in either case [65]. Considering all these recent studies, employing AI for detecting and classifying human respiration and movement under debris presents a novel approach. In this work, a debris model was constructed in a laboratory environment equipped with a UWB bi-static radar system to estimate the presence of living humans. Since the debris model, which presents a heterogeneous environment with various structural components, makes the signals more complex due to the scattering of electromagnetic waves caused by reflections and moving objects, preprocessing algorithms are needed for meaningful signal detection. Preprocessing techniques such as STFT, WT, and EMD are used to distinguish human motion from noise/clutter signals. The measurements show that the measured signal of the empty debris is different than the case in which a breathing or moving human subject is in the debris model. A raw data set was created with human subjects of different ages, genders, and heights and in different lying orientations. The number of samples was increased with a data augmentation technique (using generative adversarial nets; GANs), and the final form of the data set was obtained by employing signal processing techniques. Afterward, DL techniques such as LSTM, CNN, and temporal convolutional network (TCN) architectures were applied to the signal to detect the presence of the human. This paper is organized as follows: In Section 2, the system definition, which includes radar components and the physical debris model, is given. In Section 3, the data set is explained in detail. Section 4 presents preprocessing operations and time-frequency analysis methods. AI and DL techniques are given in Section 5, and the classification results are demonstrated in Section 6. Finally, the paper is concluded in Section 7.