RCTrans-Net: A spatiotemporal model for fast-time human detection behind walls using ultrawideband radar

Abstract

Ultrawideband (UWB) radar systems are becoming increasingly popular for detecting human presence, even through walls. Recent advancements in signal processing use deep learning techniques, which are known for their accuracy. While earlier methods focused on spatial information using Convolutional Neural Networks (CNNs), newer research highlights the importance of temporal information, such as how data peaks shift over time. This study introduces RCTrans-Net, a deep-learning architecture that combines RCNet (a Residual CNN) for spatial features with TransNet (a Transformer) for temporal features. This fusion improves human presence classification in fast-time signal processing. Tested under various conditions—different materials, body orientations, ranges, and radar heights—RCTrans-Net achieved high performance with F1-scores of 0.997±0.000 for static, 0.967±0.004 for dynamic, and 0.978±0.001 for combined scenarios. The architecture outperforms previous methods and offers real-time processing with an inference time of about one millisecond.