Kalman Filter

Python 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.

From [4]:

Installation

git clone https://github.com/ManuelZ/Kalman-Filter
cd Kalman-Filter
uv pip install -e .

Usage

from kalman_filter import Kalman

Resources

• [1] Kalman Filter from the Ground Up
• [2] Kalman Filter tutorial
• [3] filterpy package
• [4] Kalman and Bayesian filters in Python