# simondlevy/TinyEKF

Lightweight C/C++ Extended Kalman Filter with Python for prototyping

Repository: https://github.com/simondlevy/TinyEKF
Canonical: https://ross.abutalabs.com/products/tinyekf
Language: Python
License: NOASSERTION
License Family: other
Last push: 2026-04-09T02:13:38+00:00

## Health v2 (maintenance only)
Score: 66/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 76, release rhythm 35, longevity 100
- inputs: {"age_days": 3975, "days_push": 147, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1217, forks 366 (observed 2026-08-28T04:04:01.387061+00:00)

## What it is
TinyEKF is a lightweight, header-only C/C++ implementation of the Extended Kalman Filter designed for microcontrollers like Arduino and STM32 using static memory allocation. It includes a Python class for prototyping EKF models before porting them to C/C++.

## Use cases
- implement an extended kalman filter on arduino
- sensor fusion for barometer and temperature sensors
- prototype a kalman filter in python before porting to c
- run state estimation on stm32 microcontroller
- track objects with a kalman filter using opencv
- embed an ekf in embedded c code without dynamic memory allocation

## When to choose
- you need an EKF on resource-constrained microcontrollers with no malloc
- you want to prototype in Python then deploy in C/C++
- you need a small, header-only filter library for Arduino or STM32

## When to avoid
- you need an unscented or particle filter
- you need dynamically sized state vectors at runtime
- you need a full sensor fusion framework with driver support

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, simulation, sdk
- domain: robotics, embedded-systems, iot
- platform: embedded, cpp, c, python, cross-platform
- tags: kalman-filter, extended-kalman-filter, sensor-fusion, arduino, microcontroller, header-only, state-estimation, algorithms

## Member repositories
- simondlevy/TinyEKF (main) score 66

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:01.387061+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:17:09.197240+00:00, confidence not recorded.
  - readme: https://github.com/simondlevy/TinyEKF (fetched 2026-08-28T04:04:01.387061+00:00, sha d720e58962e9)
- Data as of 2026-08-30T08:39:29.467469+00:00.