Optimal Filter

Optimal filtering aims to estimate the state of a dynamic system from noisy observations, a crucial task across diverse scientific and engineering domains. Current research emphasizes developing and improving filter algorithms, including Kalman filters and their variants, recurrent neural networks, and methods leveraging variational inference and Bayesian optimization to learn optimal filter parameters. These advancements address challenges posed by nonlinear systems, high dimensionality, and the need for robust performance under varying conditions, ultimately improving accuracy and efficiency in applications ranging from robotics and signal processing to weather forecasting.