Bistatic ISAR Radar Imaging Using Missing Data Based on Compressed Sensing

Abstract

When a bistatic inverse synthetic aperture radar (ISAR) system fails to collect complete radar cross section (RCS) datasets, bistatic ISAR images are usually corrupted using the conventional Fourier transform (FT)-based imaging algorithm. To overcome this problem, this paper proposes a new bistatic ISAR image reconstruction method that includes three steps: construction of the sparse dictionaries according to the range and cross resolution units on the imaging domain and echoes can be considered as the interaction between the two-dimensional distribution of point scatterers and the sparse dictionary, construction of the observation matrix and low-dimensional observation samples are obtained, and reconstruction of scattering distribution of target using nonlinear reconstruction algorithm. To validate the reconstruction capability of the proposed method, bistatic-scattered field data using the point-scatterer model is used for bistatic ISAR image reconstruction. The results show that the proposed imaging method based on the bistatic ISAR signal model spatial reconstruction combined with the compressive sensing(CS) theory can yield high reconstruction accuracy for incomplete bistatic RCS data compared to conventional FT-based imaging methods.