Simulation and signal processing of UWB radar for human detection in complex environment

Using ultra wideband (UWB) radar in searching and rescuing at disaster relief site has widely application. Identifying life signal and locating the position of human targets are two important research areas in this regard. Comparison with detecting moving human body, the static human's weak life signal, such as breathing and heartbeat are difficult to be identified due to the complex environment interference and weak signal response. In this paper, we build the complex environment model of two human subjects trapped in the earthquake ruins and apply finite difference time domain (FDTD) method to simulate the model response. The one is supinely postured on the side and another one is laterally postured. Advancements in signal processing may allow for improved imaging and analysis of complex targets. We first apply the correlation analysis and Curvelet transform to decompose the background signal, then use singular value decomposition (SVD) to remove noise in the life signals and present the results base on FFT and ensemble empirical mode decomposition (EEMD) which combines with the Hilbert transform as the Hilbert-Huang transform (HHT) to separate and extract characteristic frequencies of breathing and heartbeat, locate the target's position. The results demonstrate that this combination of UWB impulse radar and various processing methods has potential for identifying the life characteristic from the static human's weak response and also has high accuracy for target location. It is plausible to use this approach in disaster search and rescue operations such as people trapped under building debris during earthquake, explosion or fire.