Deconvolution of weakly-sparse signals and dynamical-system identification by Gaussian message passing
Lukas Bruderer, Hampus Malmberg, Hans‐Andrea Loeliger · 2015
We use ideas from sparse Bayesian learning for estimating the (weakly) sparse input signal of a linear state space model. Variational representations of the sparsifying prior lead to algorithms that essentially amount to Gaussian message passing. The approach is extended to the case where the state space model is not known and must be estimated. Experimental results with a real-world application substantiate the applicability of the proposed method.