Enhanced Through-the-Wall Radar Imaging Based on Deep Layer Aggregation

The accurate imaging of stationary human targets in the indoor scene containing strong scatterers such as cabinets, tables, and chairs is very important for the through-the-wall radar (TWR) system. The convolution neural network (CNN) has been used to enhance radar imaging quality. In this letter, a novel multiresolution fusion network (MRFN) based on the deep layer aggregation (DLA) method is proposed for TWR imaging. The proposed MRFN can accurately localize the weak scattering human targets and provide the scattering intensity differences between the strong and weak targets. Both the simulated and real TWR data are used to evaluate the imaging performance of the proposed MRFN. The experimental results demonstrate the superiority of the proposed TWR imaging method over the existing CNN-based imaging methods.