Bayesian sequential compressed sensing in sparse dynamical systems

While the theory of compressed sensing provides means to reliably and efficiently acquire a sparse high-dimensional signal from a small number of its linear projections, sensing of dynamically changing sparse signals is still not well understood. We pursue a Bayesian approach to the problem of sequential compressed sensing and develop methods to recursively estimate the full posterior distribution of the signal.