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arXiv · 1807.03463

Constrained State Estimation -- A Review

Nesrine Amor·Ghulam Rasool·Nidhal C. Bouaynaya
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Abstract

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). Next, we present particle filters (PFs) for nonlinear state-space systems. Finally, we review constrained state estimation using various filtering techniques and highlight the advantages and disadvantages of the different constrained state estimation approaches.

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Nesrine Amor, Ghulam Rasool, Nidhal C. Bouaynaya. 2018-07-10. Constrained State Estimation -- A Review. https://arxiv.org/abs/1807.03463

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