Kalman Filter

The Kalman filter is a powerful algorithm for estimating the state of a dynamic system from noisy measurements, aiming to minimize estimation error. Current research focuses on extending its application to nonlinear and non-Gaussian systems, often integrating it with deep learning models (like neural networks and transformers) or employing variants such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), and invariant Kalman filter (InEKF) to handle complexities in diverse applications. This robust estimation technique finds widespread use in robotics, autonomous navigation, signal processing, and machine learning, improving accuracy and efficiency in various fields.