Radar coincidence imaging for extended targets in sparse Bayesian learning framework

Radar coincidence imaging is a high-resolution staring imaging technique without the limitation of relative motion between target and radar. Conventional radar coincidence imaging methods ignore the structure information of complex extended target, which limits its applications in high resolution imaging, thus an adaptive pattern-coupled sparse Bayesian learning algorithm was proposed. To model the extended target, a pattern-coupled hierarchical Gaussian prior model was introduced in sparse Bayesian learning framework, and then the algorithm alternated between steps of target reconstruction and parameter optimization under the variational Bayesian expectation maximization framework. Therefore, the reconstruction of each coefficient involved its immediate neighbors, and the parameter indicating the pattern relevance between the coefficient and its immediate neighbors was updated adaptively during the iterations. Experimental results demonstrate that the proposed algorithm can achieve high resolution imaging effectively for the extended target. © 2017, NUDT Press. All right reserved.