3-D Scattering Image Sparse Reconstruction via Radar Network

Inverse synthetic aperture radar (ISAR) can only provide two-dimensional (2-D) images to represent the target's scattering projection on the corresponding imaging planes. However, as the echo of the target from different observation angles can be achieved simultaneously, the radar network can provide 3-D scattering information about the target. In this article, a novel 3-D scattering image reconstruction method is proposed based on the radar network and compressed sensing (CS). First, the general signal model and the reconstruction conditions of the radar network 3-D reconstruction are given. Then, the 3-D scattering distribution reconstruction model is built in a CS framework, which can reconstruct the positions and coefficients of scattering centers simultaneously. Moreover, the sparse structure with three layers in radar network 3-D reconstruction is defined and a fast 3-D reconstruction algorithm is proposed. To the end, the numerical simulations under noise scenarios and the principle prototype experiments on real data are shown to demonstrate the validity of the proposed method.