Signal Processing for Improved Detection of Trapped Victims Using UWB Radar

Detection of trapped victims using ultrawideband radar is considered a highly challenging task due to multiple unknown parameters and generally very low signal-to-noise-and-clutter ratio (SNCR) conditions. In this paper, we propose a novel detection algorithm which is designed for detection of periodic motion caused by, e.g., respiratory motion of the victim for low SNCR conditions. The aim is to separate the respiratory-motion response of a trapped victim from nonstationary clutter originating from moving objects in the scene of interest. The algorithm performs stationary-clutter removal, high-level noise, and nonstationary-clutter suppression, indicates presence of the trapped victim, and estimates its range. The performance of the algorithm is investigated, both by means of simulation and experimental verification. The results show improved detection capabilities in low SNCR over an existing algorithm proposed by Zaikov