FilterPy is a Python library for Kalman filtering and related state-estimation methods. It implements standard, extended, and unscented Kalman filters, along with Kalman smoothers. The package also includes particle filters, least-squares filters, fading-memory filters, g-h filters, discrete Bayes methods, and H-infinity tools. Its code favors readability and close correspondence with the underlying equations. NumPy and SciPy handle the numerical work, with Matplotlib commonly used for visualization. The library accompanies the book Kalman and Bayesian Filters in Python and is useful for learning, prototyping, tracking, navigation, and estimation experiments.
Features
- Standard, extended, and unscented Kalman filters
- Kalman smoothing and state estimation
- Particle and discrete Bayes filtering
- Least-squares, fading-memory, and g-h filters
- NumPy and SciPy numerical integration
- Companion educational material and examples
Categories
LibrariesLicense
MIT LicenseFollow FilterPy
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