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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 sparsedoi:10.1155/2020/9202654 doaj:19e4ff42aef1493b9eca7d901a06aa2d fatcat:fobedem4rnff5aaxhfwww5ffwq