Talks and Poster Presentations (without Proceedings-Entry):
C. Mecklenbräuker, P. Gerstoft, E. Zöchmann, H. Yao, P. M. Shearer:
"Sequential Sparse Signal Estimation from Array Data";
Talk: Böhme und Fettweis Workshop,
Ruhr-Universität Bochum (invited);
A sequential Bayesian approach to density evolution for sparse source reconstruction is proposed and analysed which alternatingly solves a generalized LASSO problem and its dual. 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 is the solution to a generalized LASSO problem. The posterior Laplace-like density at step k is approximated by the corresponding dual solution. The posterior density at step k leads to the prior density at k + 1 by applying a motion model. Thus, a sequence of generalized LASSO problems is solved for estimating the temporal evolution of a sparse source field.
source tracking, sparsity, sequential estimation
Created from the Publication Database of the Vienna University of Technology.