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C. Mecklenbräuker, P. Gerstoft, E. Zöchmann, H. Yao, P. M. Shearer:
"Sequential Sparse Signal Estimation from Array Data";
Vortrag: Böhme und Fettweis Workshop, Ruhr-Universität Bochum (eingeladen); 12.09.2014.

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