This project is an educational resource and toolkit for implementing Bayesian estimation and Kalman filters in Python. It provides a framework for constructing linear and non-linear filters to estimate the state of dynamic systems by combining noisy sensor data with mathematical process models.
The library focuses on probabilistic state estimation, utilizing recursive Bayesian updating and state-space mathematical modeling to refine beliefs about system states. It includes utilities for simulating dynamic systems, allowing users to generate synthetic trajectories and sensor observations to validate tracking algorithms against known ground truth data.
The collection covers core concepts in control systems engineering, robotics navigation, and sensor data fusion. It is structured as a comprehensive guide that combines theoretical explanations with practical code implementations for calculating probability distributions and managing uncertainty in dynamic environments.
