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2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers
This paper considers the problem of recovering time-varying sparse signals from dramatically undersampled measurements. A probabilistic signal model is presented that describes two common traits of time-varying sparse signals: a support set that changes slowly over time, and amplitudes that evolve smoothly in time. An algorithm for recovering signals that exhibit these traits is then described. Built on the belief propagation framework, the algorithm leverages recently developed approximatedoi:10.1109/acssc.2010.5757677 fatcat:eawfcnstlveynh2e3gfkwp66di