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• W2046690972 abstract "In this letter, we propose a multi-task compressive sensing algorithm for the reconstruction of clustered sparse entries based on hierarchical Bayesian framework. By extending a paired spike-and-slab prior to a general multi-task model, the proposed algorithm has the capability of modeling both inter-task and intra-task dependencies of the observation data. The latter is achieved by imposing a clustered prior on non-zero entries and finds applications in radar where targets exhibit spatial extent. Simulation results verify that the proposed algorithm outperforms state-of-the-art group sparse Bayesian learning algorithms." @default.
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• W2046690972 date "2015-04-01" @default.
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• W2046690972 title "Multi-Task Bayesian Compressive Sensing Exploiting Intra-Task Dependency" @default.
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• W2046690972 doi "https://doi.org/10.1109/lsp.2014.2360688" @default.
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