Kalmanfiltermodeller
Kalmanfiltermodeller is a software framework designed to construct and estimate state-space models using Kalman filtering techniques. It focuses on inferring hidden state variables from noisy observations by applying recursive estimation methods derived from the Kalman filter and its extensions.
The framework provides a modular architecture with components for defining system dynamics, specifying process and measurement
Supported algorithms typically include the Kalman filter for linear Gaussian models, the extended Kalman filter for
Common modeling capabilities include time-varying state-transition and observation matrices, multi-dimensional states, and probabilistic handling of uncertainties.
Kalmanfiltermodeller is commonly employed in robotics, navigation, finance, and engineering research, where reliable state estimation from
Related topics include Kalman filter, state-space model, Bayesian filtering, EM algorithm, and nonlinear filtering.