Sequential Sparse System Estimation for Linear Systems with Nonlinear Observations

This paper presents an online (sequential) method for sparse system estimation in dynamic systems with linear Gaussian state evolution and nonlinear and/or non-Gaussian observations. Estimation is based on an Automatic Relevance Determination (ARD) prior for the elements of the system matrix A, with an online Expectation-Maximization algorithm used within a particle filter for sequential estimation. The method is demonstrated to work effectively on an example system.