KalmanFilterungen
KalmanFilterungen refer to a family of estimation methods designed to infer the hidden state of a dynamic system from noisy measurements. Originating from the work of Rudolf Kalman, these methods are used in engineering, navigation, robotics, and signal processing to fuse information from multiple sensors and track evolving quantities over time.
The classic Kalman filter assumes a linear, Gaussian state-space model. The hidden state x_k evolves as x_k
Beyond the linear case, extensions address nonlinearity: the extended Kalman filter linearizes around the current estimate,
Applications include navigation (inertial/GPS fusion), aerospace tracking, robotics, finance, and environmental monitoring. Practical implementation requires initial