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Sequential Bayesian Sparse Signal Reconstruction Using Array Data

C. F. Mecklenbrauker ; P. Gerstoft ; Ashkan Panahi (Institutionen för signaler och system, Signalbehandling) ; Mats Viberg (Institutionen för signaler och system, Signalbehandling)
Ieee Transactions on Signal Processing (1053-587X). Vol. 61 (2013), 24, p. 6344-6354.
[Artikel, refereegranskad vetenskaplig]

In this paper, the sequential reconstruction of source waveforms under a sparsity constraint is considered from a Bayesian perspective. Let the wave field, which is observed by a sensor array, be caused by a spatially-sparse set of sources. A spatially weighted Laplace-like prior is assumed for the source field and the corresponding weighted Least Absolute Shrinkage and Selection Operator (LASSO) cost function is derived. After the weighted LASSO solution has been calculated as the maximum a posteriori estimate at time step, the posterior distribution of the source amplitudes is analytically approximated. The weighting of the Laplace-like prior for time step is then fitted to the approximated posterior distribution. This results in a sequential update for the LASSO weights. Thus, a sequence of weighted LASSO problems is solved for estimating the temporal evolution of a sparse source field. The method is evaluated numerically using a uniform linear array in simulations and applied to data which were acquired from a towed horizontal array during the long range acoustic communications experiment.

Nyckelord: Bayesian estimation, sequential estimation, sparsity, weighted LASSO, towed array, lasso, selection

Denna post skapades 2014-01-20. Senast ändrad 2015-05-08.
CPL Pubid: 192887


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Institutionen för signaler och system, Signalbehandling (1900-2017)


Elektroteknik och elektronik

Chalmers infrastruktur