# TinyEKF

**Repository Path**: andy_wsj/TinyEKF

## Basic Information

- **Project Name**: TinyEKF
- **Description**: Lightweight C/C++ Extended Kalman Filter with Python for prototyping
- **Primary Language**: C
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2021-03-13
- **Last Updated**: 2024-05-31

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# TinyEKF: Lightweight C/C++ Extended Kalman Filter with Python for prototyping

<img src="fusion.png" width=500>

TinyEKF is a simple C/C++ implementation of the [Extended Kalman Filter](http://home.wlu.edu/~levys/kalman_tutorial/) 
that is general enough to use on different projects.  In order to make it
practical for running on Arduino, STM32, and other microcontrollers, it uses
static (compile-time) memory allocation (no "new" or "malloc").  The
**examples** folder includes an Arduino example of sensor fusion.  The
**extras/python** folder includes an abstract Python class that you can use to
prototype your EKF before implementing it in C/C++. The **extrasc/c** folder
contains a "pure C" example from the literature.

Arduino users can simply install or drag the whole TinyEKF folder into their Arduino libraries folder. 
The **examples/SensorFusion** folder contains a little sensor fusion example using a 
[BMP180 barometer](https://www.sparkfun.com/products/11824) and 
[LM35 temperature sensor](http://www.robotshop.com/en/dfrobot-lm35-linear-temperature-sensor.html).
I have run this example on an Arduino Uno and a Teensy 3.2. The BMP180, being an I^2C sensor, should be connected
to pins 4 (SDA) and 5 (SCL) of the Uno, or pins 18 (SDA) and 19 (SCL) of the Teensy.  For other Arduino boards,
consult the [documentation](https://www.arduino.cc/en/Reference/Wire) on the Wire library. The analog output
from the LM35 should go to the A0 pin of your Arduino or Teensy.

In addition to the class definition, the **extras/python** folder has an example of mouse tracking, using OpenCV. 
So you will have to install OpenCV to run this example. There is also a sensor-fusion example in this folder,
which uses the [RealtimePlotter](https://github.com/simondlevy/RealtimePlotter) package, requiring
Matplotlib.