Extended Target Reconstruction for Real Aperture Radar Using Sparse and 2-D High-Order Gradient Hybrid Prior Bayesian Method

Abstract

The reconstruction of the scale and shape information of extended targets is a major challenge for real aperture radar (RAR). Due to the limitation of angular resolution, the reconstruction of extended targets is inaccurate. To this end, a sparse and 2-D high-order gradient (S-2DHG) hybrid prior-based Bayesian method was proposed for RAR to reconstruct the extended targets by introducing a novel scale-constrained prior into the framework of existing hybrid priors. On the one hand, the proposed 2-D high-order gradient (2DHG) prior establishes interconnections among multiple adjacent units in both the range and azimuth directions during the reconstruction of the scattering coefficient unit and the current target. This interconnection facilitates the formation of a 2DHG prior, which effectively mitigates the influence of sidelobes in both range and azimuth. The sparse prior helps to alleviate the resolution loss of the 2DHG prior. On the other hand, the proposed Bayesian solution framework introduces a Jeffery uninformative prior, which can realize the adaptive update of sparse scale prior weight parameters, reducing the number of manually selected parameters. Simulation and experimental results present superior data fidelity and edge preservation ability of the proposed method, which can accurately reconstruct the scale information of the extended targets.