kalmanjs
Version:
A small library implementing the principle of Kalman filters, without any dependencies, to filter out noise in 1D systems.
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Markdown
Javascript based Kalman filter for 1D data. Sometimes you need a simple noise filter without any dependencies; for those cases *Kalman.js* is perfect.
I wrote two blog posts on explaining Kalman filters in general and applying them on noisy data in particular:
* [KalmanJS, Lightweight Javascript Library for Noise filtering](https://www.wouterbulten.nl/blog/tech/lightweight-javascript-library-for-noise-filtering/)
* [Kalman filters explained: Removing noise from RSSI signals](https://www.wouterbulten.nl/blog/tech/kalman-filters-explained-removing-noise-from-rssi-signals/)
Please see the blog post ([KalmanJS, Lightweight Javascript Library for Noise filtering](https://wouterbulten.nl/blog/tech/lightweight-javascript-library-for-noise-filtering/)) for more information about using this library. Any questions can be posted there as comments.
## Installation
The KalmanJS library is a small javascript library and can easily be integrated in to your project manually. Alternatively, the library can be included using npm.
### In the browser
Include the `kalman.js` or `kalman.min.js` from the `dist` folder on your webpage, the filter can then be used directly.
```html
<script src="kalman.min.js" type="text/javascript"></script>
<script type="text/javascript">
var kf = new KalmanFilter();
console.log(kf.filter(3));
console.log(kf.filter(2));
console.log(kf.filter(1));
</script>
```
Should output (with default settings):
```
3
2.3333333333333335
1.5000000000000002
```
### Node (es6)
`npm install kalmanjs`
```javascript
import KalmanFilter from 'kalmanjs';
const kf = new KalmanFilter();
console.log(kf.filter(3));
console.log(kf.filter(2));
console.log(kf.filter(1));
```
Should output (with default settings):
```
3
2.3333333333333335
1.5000000000000002
```
### Node (es5)
`npm install kalmanjs`
```javascript
var KalmanFilter = require('kalmanjs');
var kf = new KalmanFilter();
console.log(kf.filter(3));
console.log(kf.filter(2));
console.log(kf.filter(1));
```
Should output (with default settings):
```
3
2.3333333333333335
1.5000000000000002
```
## Applying the filter on a dataset
Using the filter is simple. First we create a simple dataset with random noise:
```javascript
//Generate a simple static dataset
var dataConstant = Array.apply(null, {length: dataSetSize}).map(function() {
return 4;
});
//Add noise to data
var noisyDataConstant = dataConstant.map(function(v) {
return v + randn(0, 3);
});
```
Then we apply the filter iteratively on each data element:
```javascript
//Apply kalman filter
var kalmanFilter = new KalmanFilter({R: 0.01, Q: 3});
var dataConstantKalman = noisyDataConstant.map(function(v) {
return kalmanFilter.filter(v);
});
```
See [this blog post](https://wouterbulten.nl/blog/tech/lightweight-javascript-library-for-noise-filtering/) for screenshots and more examples.
This project was part of my research on indoor localization. Please see my [paper](http://ieeexplore.ieee.org/document/7471364) or this [presentation](https://www.wouterbulten.nl/blog/tech/iotdi-ic2e-conference-presentation-human-slam/) for more information. You can use the following reference if you want to cite my paper:
> W. Bulten, A. C. V. Rossum and W. F. G. Haselager, "Human SLAM, Indoor Localisation of Devices and Users," *2016 IEEE First International Conference on Internet-of-Things Design and Implementation (IoTDI)*, Berlin, 2016, pp. 211-222. doi: 10.1109/IoTDI.2015.19 [URL](http://ieeexplore.ieee.org/document/7471364)
Or, if you prefer in BibTeX format:
```tex
@INPROCEEDINGS{7471364,
author={W. Bulten and A. C. V. Rossum and W. F. G. Haselager},
booktitle={2016 IEEE First International Conference on Internet-of-Things Design and Implementation (IoTDI)},
title={Human SLAM, Indoor Localisation of Devices and Users},
year={2016},
pages={211-222},
keywords={RSSI;data privacy;indoor environment;ubiquitous computing;FastSLAM;RSSI update;SLAC algorithm;device RSSI;device indoor localisation;device location;device position;environment noise;human SLAM;nontrivial environment;received signal strength indicator;simultaneous localisation and configuration;smart space;user indoor localisation;user motion data;user privacy;Estimation;Performance evaluation;Privacy;Simultaneous localization and mapping;Privacy;Simultaneous localization and mapping;Smart Homes;Ubiquitous computing;Wireless sensor networks},
doi={10.1109/IoTDI.2015.19},
month={April},}
```
Kalman filters can be useful in a broad range of projects. Regularly I get questions whether KalmanJS is available in other languages than Javascript and sometimes another library is available. I would encourage searching for it if you require another implementation. For convenience, this repository contains a *contrib* folder with user-submitted implementations in other languages.
MIT License
Copyright (c) 2018 Wouter Bulten
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