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# denyssene / SimpleKalmanFilter Public

A basic implementation of Kalman Filter for single variable models.

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## Latest commit

`add getEstimateError`
`16478b0`

## Files

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# Simple Kalman Filter Library -

This is a basic kalman filter library for unidimensional models that you can use with a stream of single values like barometric sensors, temperature sensors or even gyroscope and accelerometers.

• Take a look at this youtube video to see the Kalman Filter working on a stream of values!

Special thanks to Professor Michel van Biezen and his amazing work in http://www.ilectureonline.com/

## Repository Contents

• /examples - Example sketches for the library (.ino). Run these from the Arduino IDE.
• /src - Source files for the library (.cpp, .h).
• keywords.txt - Keywords from this library that will be highlighted in the Arduino IDE.
• library.properties - General library properties for the Arduino package manager.

## Basic Usage

• e_mea: Measurement Uncertainty - How much do we expect to our measurement vary
• e_est: Estimation Uncertainty - Can be initilized with the same value as e_mea since the kalman filter will adjust its value.
• q: Process Variance - usually a small number between 0.001 and 1 - how fast your measurement moves. Recommended 0.01. Should be tunned to your needs.
``` SimpleKalmanFilter kf = SimpleKalmanFilter(e_mea, e_est, q);

while (1) {
float x = analogRead(A0);
float estimated_x = kf.updateEstimate(x);

// ...
}
```

## Example Briefs

• BasicKalmanFilterExample - A basic example reading a value from a potentiometer in A0 and SimpleKalmanFilter class to generate estimates.
• AltitudeKalmanFilterExample - Uses a BMP180 barometric sensor and the SimpleKalmanFilter class to estimate the correct altitude.

## Version History

This is an open source project!

If you have any questions or concerns on licensing, please contact denys.sene@gmail.com.

A basic implementation of Kalman Filter for single variable models.