[Back]

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), Pisa, Italia; 06-17-2015 - 06-19-2015; in: "2015 3rd International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar, and Remote Sensing (CoSeRa)", (2015), ISBN: 978-1-4799-7420-7; 5 pages.

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

http://dx.doi.org/10.1109/CoSeRa.2015.7330277

http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7330277

Project Head Christoph Mecklenbräuker:
Christian Doppler Lab "Funktechnologien für nachhaltige Mobilität"