Simultaneous sparse approximation via greedy pursuit

By Joel A. Tropp, Anna C. Gilbert, Martin J. Strauss. Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., 2005

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Millimeter Wave Beam-Selection Using Out-of-Band Spatial Information (2017, background): “lem [19] to leverage training from all active subcarriers.”
Spectrum sensing in cognitive radio (2016, methodology): “In order to exploit the additional knowledge that the vectors r̂x| νnν ν=ν1 have the same support (they are jointly sparse with regard to the time delay), we propose to use an extension of OMP called simultaneous orthogonal matching pursuit (SOMP) [69].”
Distributed Compressive Sensing (2009, unknown): “ExamplesofJSM-2scenariosinclude MIMOcommunication[34]andaudiosignalarrays;thesignalsmaybesparseintheFourierdomain, forexample,yetmultipathresultingfromdifferingpropagationpathscausesdifferentattenuations amongthefrequencycomponents.”
Distributed Compressed Sensing of Jointly Sparse Signals (2005, background): “We plot the probability of perfect reconstruction via DCS - SOMP ( solid lines ) and independent CS reconstruction ( dashed lines ) as a function of the number of measurements per signal M and the num - ber of signals J .”
Multitask Compressive Sensing (2009, methodology): “In addition, a performance comparison between S-OMP [47] and MT-BCS has been presented, demonstrating superior performance of the Bayesian algorithms.”