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 noise, and configuring estimation algorithms. It supports both linear and nonlinear models and can operate in batch mode or real-time (online) mode as data streams in.

Supported algorithms typically include the Kalman filter for linear Gaussian models, the extended Kalman filter for nonlinearities, the unscented Kalman filter, and related smoothing methods. Many implementations also offer parameter estimation workflows, such as maximum likelihood estimation and Expectation-Maximization to learn unknown dynamics or noise characteristics.

Common modeling capabilities include time-varying state-transition and observation matrices, multi-dimensional states, and probabilistic handling of uncertainties. Users can define custom process and measurement equations and integrate external data sources.

Kalmanfiltermodeller is commonly employed in robotics, navigation, finance, and engineering research, where reliable state estimation from noisy data is essential. The project emphasizes clear documentation, reproducible workflows, and interoperability with standard data formats and analysis environments.

Related topics include Kalman filter, state-space model, Bayesian filtering, EM algorithm, and nonlinear filtering.