Kalman-and-Bayesian-Filters-in-Python
An interactive Jupyter Notebook book teaching Kalman and Bayesian filters through Python code and practical examples.
What is Kalman-and-Bayesian-Filters-in-Python?
Kalman and Bayesian Filters in Python is an interactive educational book that teaches state estimation and filtering techniques using Python and Jupyter Notebooks. It provides a gentle introduction to Kalman filters, extended Kalman filters, unscented Kalman filters, particle filters, and other Bayesian filtering methods, focusing on practical implementation rather than formal proofs. The book solves the problem of noisy sensor data and uncertain system knowledge by showing how to optimally estimate system states over time.
Hobbyists, students, and working engineers who need to implement filtering algorithms for projects involving sensors, tracking, or time-series data. It's particularly valuable for those in robotics, computer vision, IoT, or any field dealing with noisy measurements who want an accessible entry point to Bayesian filtering.
Developers choose this resource because it offers an interactive, code-first approach that demystifies complex filtering concepts through immediate experimentation. Unlike traditional academic textbooks, it provides complete solutions to exercises, real-world examples, and a supporting Python library, making it the most practical and accessible way to learn these techniques.
Overview
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
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