Talks and Poster Presentations (without Proceedings-Entry):
C. Mecklenbräuker, P. Gerstoft:
"Sequential Bayesian Reconstruction of Sparse Source from Sensor Array Data";
Talk: Information Theory and Applications Workshop (ITA 2014),
San Diego (A), USA (invited);
The sequential Bayesian reconstruction of sparse source waveforms from sensor array data is analyzed. Acoustic waves are observed by a sensor array. The waves are emitted by a spatially-sparse set of sources. A weighted Laplace-like prior is assumed for the sources such that the maximum a posteriori source estimate at the current time step can be approximated from the weighted LASSO. The new weighting for time step k+1 is defined from a fit to the approximated posterior distribution at the previous time step k. Thus, a sequence of weighted LASSO problems is solved for estimating the temporal evolution of a sparse source field. Finally, we explore M. E. Tipping's approach to fast marginal likelihood maximization for sparse Bayesian models for sequential source waveform reconstruction.
sparsity, sequential, LASSO, convex optimization, duality,
Created from the Publication Database of the Vienna University of Technology.