Zustandsschätzungsmodell

Zustandsschätzung, often translated as state estimation, is a fundamental concept in engineering and control theory. It refers to the process of determining the internal state of a dynamic system when direct measurement of all state variables is impossible or impractical. The state of a system is a minimal set of variables that fully describes its behavior at any given time. For instance, in a mechanical system, the state might include position and velocity.

Since not all of these state variables can always be measured directly, state estimation techniques use available measurements, along with a mathematical model of the system's dynamics, to infer the unmeasured states. This process typically involves processing noisy sensor data to produce a more accurate and reliable estimate of the system's true state. Common applications include navigation, robotics, process control, and econometrics.

Various algorithms exist for state estimation, with the Kalman filter being one of the most widely recognized and utilized. The Kalman filter is an optimal recursive estimator for linear systems with Gaussian noise. For non-linear systems, extensions like the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF) are employed. Other methods, such as particle filters, are used for systems with highly non-linear dynamics or non-Gaussian noise. The goal of state estimation is to provide an accurate and timely understanding of the system's condition, which is crucial for effective decision-making and control.