balzer82/Kalman resource
Some Python Implementations of the Kalman Filter observed · 2026-08-28
Health v2 · maintenance only
32/100
- Activity 0
- Release rhythm 35
- Longevity 100
Flags: no_releases no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 4587
- days_rel: n/a
- days_push: 884
- n_releases_24m: 0
Adoption not part of the score
1142 stars · 371 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A collection of Jupyter Notebook implementations of Kalman Filters (constant velocity, constant acceleration, 2D/3D, adaptive, extended) in Python, with explanatory blog posts and videos. It serves as an educational resource for learning state estimation and sensor fusion rather than a production library.
Use cases
- learn how a kalman filter works with python examples
- sensor fusion of gps and accelerometer data
- track a ball in 3d space with a kalman filter
- implement an extended kalman filter tutorial
- estimate position when gps signal is lost in a tunnel
- adaptive measurement covariance kalman filter example
When to choose
- you want to learn or teach Kalman filtering with runnable notebooks
- you need reference implementations to adapt for sensor fusion prototypes
When to avoid
- you need a production-grade, maintained filtering library with an API
- you need a package with a license for commercial redistribution
Facets
learning-resource · maturity maintenance
simulation data-science math tutorials data-science python cross-platform kalman-filter jupyter-notebooks sensor-fusion state-estimation educational algorithms
1 source
- readme: https://github.com/balzer82/Kalman · fetched 2026-08-28 · f4149f8b6cb3
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| balzer82/Kalman | main | 32 |
For agents
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem