Tracking sparse signal sequences from nonlinear/non-Gaussian measurements and applications in illumination-motion tracking

In this work, we develop algorithms for tracking time sequences of sparse spatial signals with slowly changing sparsity patterns, and other unknown states, from a sequence of nonlinear observations corrupted by (possibly) non-Gaussian noise. A key example of the above problem occurs in tracking moving objects across spatially varying illumination changes, where motion is the small dimensional state while the illumination image is the sparse spatial signal satisfying the slow-sparsity-pattern-change property.