Nonlinear State Estimation

Nonlinear state estimation aims to accurately determine the hidden state of a system from noisy measurements, a crucial task in diverse fields like robotics and geosciences. Current research focuses on improving existing Kalman filter variants (e.g., extended, unscented) through novel frameworks and incorporating deep learning techniques, including neural networks and Koopman operator methods, to handle complex nonlinearities. These advancements enhance the accuracy and robustness of state estimations, particularly in scenarios with high dimensionality, non-Gaussian noise, or limited measurements, leading to improved control and prediction capabilities in various applications.