Improved Bayesian ISAR Imaging by Learning the Local Structures of the Target Scene

In inverse synthetic aperture radar (ISAR) imaging, some weak scatterers might be missing in the results obtained by conventional compressive sensing (CS)-based methods. This paper proposes a novel method to preserve the weak scatterers by exploiting the local structures of the target scene statistically in the imaging process. Particularly, we partition the target scene into many overlapping patches, and the local structure in each patch is represented by the covariance matrix, which is learned from the measurements. Simultaneously, the Dirichlet process (DP) priors are exploited to accomplish the clustering of the local structures. Under the sparse Bayesian learning framework, the scatterers are modeled hierarchically, and we tailor the variational Bayesian inference (VBI) to incorporate the structural information into the reconstruction of the target image, where the weak scatterers can be preserved. Comprehensive experiments based on synthetic data, electromagnetic (EM) simulated data, and real data have validated the performance of the proposed method.