Enhanced Vortex Wavefront Modulated Radar Forward-Looking Variational Bayesian Hierarchical Structural Sparse Imaging by Leveraging the Continuity of Target Scene

Abstract

The vortex wave featuring specific wavefronts and orthogonal orbital angular momentum (OAM) modes has demonstrated broad prospects in radar forward-looking imaging applications. However, the Bessel function modulation effect arising from the physical property of vortex waves and the limited OAM modes affect the imaging performance. To solve this issue, we present an innovative vortex wavefront modulated radar (VWMR) forward-looking imaging method by leveraging the underlying continuity of the scatterers within the target scene to achieve improved VWMR imaging reconstructions through a hierarchical Bayesian framework. An extended 2-D structural spike-and-slab hierarchical Bayesian prior model is proposed to statistically promote spatial continuity of the target scene, where the support and the scattering coefficient values are both correlated with their neighbors. Specifically, the coefficient values exhibit correlation through a soft coupling mechanism that shares hyperparameters among neighboring coefficients. The support of the target area is enforced to be dependent on its immediate neighbors, further promoting the zero or nonzero scatterers to gather in a spatial location-correlation behavior. The variational Bayesian expectation-maximization method is exploited for the approximate posterior inference of the latent variables and the estimation of the model parameters. Comprehensive experimental results using synthetic, electromagnetic, and measured data validate that the proposed method offers superior reconstruction performance over other reported VWMR imaging algorithms.