Extended Kalman Filter
The Extended Kalman Filter (EKF) is a nonlinear version of the Kalman Filter, a mathematical algorithm used for state estimation in dynamic systems. It linearizes the system's nonlinear equations around the current state estimate using a first-order Taylor series expansion, enabling it to handle systems with nonlinear dynamics or measurement models. It is widely applied in robotics, navigation, and control systems to estimate unknown variables from noisy sensor data.
Developers should learn the EKF when working on projects involving state estimation in nonlinear systems, such as autonomous vehicles, drones, or robotic localization, where sensor data is imperfect and models are not linear. It is particularly useful in real-time applications requiring recursive filtering to update estimates as new measurements arrive, providing a computationally efficient alternative to more complex nonlinear filters like the Unscented Kalman Filter in many cases.