Joint State
Joint state estimation focuses on simultaneously estimating multiple related variables within a system, improving accuracy and robustness compared to estimating them independently. Current research emphasizes Bayesian methods, including variations of Kalman filtering and particle filtering, often coupled with probabilistic numerical techniques to handle uncertainty in nonlinear systems and noisy data. These advancements are crucial for applications ranging from robotics and target tracking to battery health management and biological studies, enabling more accurate modeling and prediction in complex dynamic environments.
Papers
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