Sparse Bayesian Perspective for Radar Coincidence Imaging with Model Errors

Bo Fan, Xiaoli Zhou, Shuo Chen, Zhijie Jiang, Yongqiang Cheng
2020 Mathematical Problems in Engineering  
Sparsity-driven methods are commonly applied to reconstruct targets in radar coincidence imaging (RCI), where the reference matrix needs to be computed precisely and the prior knowledge of the accurate imaging model is essential. Unfortunately, the existence of model errors in practical RCI applications is common, which defocuses the reconstructed image considerably. Accordingly, this paper aims to formulate a unified framework for sparsity-driven RCI with model errors based on the sparse
more » ... an approach. Firstly, a parametric joint sparse reconstruction model is built to describe the RCI when perturbed by model errors. The structured sparse Bayesian prior is then assigned to this model, after which the structured sparse Bayesian autofocus (SSBA) algorithm is proposed in the variational Bayesian expectation maximization (VBEM) framework; this solution jointly realizes sparse imaging and model error calibration. Simulation results demonstrate that the proposed algorithm can both calibrate the model errors and obtain a well-focused target image with high reconstruction accuracy.
doi:10.1155/2020/9202654 doaj:19e4ff42aef1493b9eca7d901a06aa2d fatcat:fobedem4rnff5aaxhfwww5ffwq