Nonlinear Bayesian state estimation: A review of recent developments

Online estimation of the internal states is a perquisite for monitoring, control, and fault diagnosis of many engineering processes. A cost effective approach to monitor these variables in real time is to employ model-based state estimation techniques. Dynamic model-based state estimation is a rich and highly active area of research and many novel approaches have emerged over the last few years. In this paper, we review various recent developments in the area of nonlinear state estimators from a Bayesian perspective. In particular, we focus on the constrained state estimation (including systems modeled using differential-algebraic equations), the handling of multi-rate and delayed measurements and recent advances in model parameter estimation. Recent advances on the stability analysis of the estimation error dynamics are also briefly discussed. The review aims to provide an integrated view of important ideas, from the authors' perspective that have driven the research in this area in recent years.