Radar coincidence imaging (RCI) is a recently developed concept of radar imaging, which introduces the optical coincidence imaging to traditional microwave imaging. Conventional RCI methods ignore the structure inform...
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Radar coincidence imaging (RCI) is a recently developed concept of radar imaging, which introduces the optical coincidence imaging to traditional microwave imaging. Conventional RCI methods ignore the structure information of complex extended target, which limits its applications in high resolution imaging, thus an adaptive clustered sparse bayesian learning algorithm is proposed in this study. To exploit the continuity of extended target, a hierarchical correlated Gaussian prior model is introduced to take into account both the sparse prior and the cluster prior, and then the algorithm alternates between steps of target reconstruction and parameter optimisation under the variational bayesian expectation-maximisation framework. Therefore, the reconstruction of each coefficient involves its immediate neighbours, and the parameter indicating the pattern relevance among neighbouring scatterers is updated adaptively during the iterations. Experimental results demonstrate that the proposed algorithm could realise high resolution imaging effectively for extended target.
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