Aspect Dependent-based Ghost Suppression for Extended Targets in Through-the-wall Radar Imaging under Compressive Sensing Framework

Abstract

Several approaches have been proposed to suppress multipath ghost in through-the-wall radar imaging (TWRI). One classical approach, called Aspect Dependent (AD), exploits locations of ghosts in the images without demanding prior knowledge of the reflecting geometry. This operation strategy makes the method superior over multipath exploitation-based approaches. However, the AD method assumes a point target that emulates unreal environment. Therefore, reconstructing extended targets with this method leads to incorrect scene interpretation. This work proposes a ghost suppression method for extended targets based on the AD feature that exploits duo sub-apertures. Firstly, we evaluate the best suppression method using a performance metric called relative clutter peak. Next, the evaluated method is extended to encompass the target extent during sub-images reconstruction. Following this strategy, an effective image fusion method suitable for extended targets is proposed. The method considers pixel neighborhood to effectively recover the given extended target. Simulation results show that the proposed method significantly improves signal-to-clutter ratio and relative clutter peak by 8.8% and 23.8%, respectively, relative to the existing AD based methods under point target assumption.