Extended Kalman Filter

The Extended Kalman Filter (EKF) is a recursive algorithm used for state estimation in nonlinear systems, aiming to optimally combine predicted states with noisy measurements to improve accuracy. Current research emphasizes its application in diverse fields, including robotics (e.g., LiDAR-inertial odometry, multi-robot localization), autonomous vehicles, and spacecraft navigation, often integrating EKFs with other techniques like neural networks, Markov models, or factor graph optimization for enhanced robustness and efficiency. This versatile filter's ability to handle noisy data and nonlinear dynamics makes it a crucial tool for improving the accuracy and reliability of state estimation across numerous scientific and engineering applications.