Constrained State Estimation -- A Review

Understand

The real-world applications in signal processing generally involve estimating the system state or parameters in nonlinear, non-Gaussian dynamic systems.

• The estimation problem may get even more challenging when there are physical constraints on the system state.
• This tutorial-style paper reviews the Bayesian state estimation for (non)linear state-space systems and introduces the formulation of constrained state estimation in such scenarios.
• Specifically, we start by providing a review of unconstrained state estimation using Kalman filters (KF) for the linear systems and their extensions for nonlinear state-space systems, including extended Kalman filters (EKF), unscented Kalman filters (UKF), and ensemble Kalman filters (EnKF).