Talks and Poster Presentations (with Proceedings-Entry):
E. Zöchmann, P. Gerstoft, C. Mecklenbräuker:
"Density Evolution of Sparse Source Signals";
Poster: International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar, and Remote Sensing (CoSeRa),
- 06-19-2015; in: "2015 3rd International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar, and Remote Sensing (CoSeRa)",
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.
sequential estimation, Bayesian estimation, sparsity, generalized LASSO
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
Electronic version of the publication:
Project Head Christoph Mecklenbräuker:
Christian Doppler Lab "Funktechnologien für nachhaltige Mobilität"
Created from the Publication Database of the Vienna University of Technology.