Tillståndsestimation

Tillståndsestimation, often translated as state estimation, is a process in engineering and mathematics that involves determining the internal state of a dynamic system from a series of incomplete and noisy measurements. The state of a system refers to a set of variables that fully describe its condition at a given time. These variables might include position, velocity, temperature, pressure, or any other relevant quantities.

Since it is rarely possible to directly measure all the state variables of a complex system, and measurements are often corrupted by noise, estimation techniques are employed to infer the most likely state. This is particularly crucial in control systems, navigation, signal processing, and many other fields where understanding and predicting system behavior is essential.

Common methods for state estimation include the Kalman filter, which is optimal for linear systems with Gaussian noise, and its non-linear extensions like the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). Particle filters, which use Monte Carlo methods, are employed for highly non-linear systems or those with non-Gaussian noise. The core idea behind these techniques is to combine a model of the system's dynamics with the incoming measurements to produce a refined estimate of the system's state over time.