A straightforward implementation of a Kalman Filter for linear dynamic systems. The Kalman class encapsulates the core steps of the filtering process:
- Initialization: Set up the state, covariance, and system matrices.
- Prediction: Propagate the current state and uncertainty forward using the system dynamics (with optional control input).
- Update: Incorporate new measurements to correct the state estimate and reduce uncertainty.
Inline comments and references explain the theory and practical use of the Kalman Filter.
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