# kalmanjs-updated

> A small library implementing the principle of Kalman filters, without any dependencies, to filter out noise in 1D systems.

Latest version **1.0.1** (published 2019-01-15) · LGPL-3.0 license · 0 weekly downloads

## Install

```sh
npm install kalmanjs-updated
pnpm add kalmanjs-updated
yarn add kalmanjs-updated
bun add kalmanjs-updated
```

## Health

**Score 15/100 (F)** — status: abandoned.

Positive: no vulnerabilities.

Warnings: low downloads; no types; no esm support.

Negative: abandoned; low maintenance score.

## Facts

| | |
|---|---|
| Version | 1.0.1 |
| Published | 2019-01-15 |
| First published | 2019-01-15 |
| Weekly downloads | 0 |
| License | LGPL-3.0 |
| TypeScript types | none |
| Module format | CommonJS |
| Dependencies | 0 |
| Unpacked size | 141.2 KB |
| Known vulnerabilities | 0 |
| Install scripts | no |
| GitHub stars | 0 |
| Author | Wouter Bulten |
| Maintainers | skit456 |
| Keywords | kalman, filter, math, noise, javascript |

## Links

- npm: https://www.npmjs.com/package/kalmanjs-updated
- Repository: https://github.com/scbrady/kalmanjs
- Issues: https://github.com/scbrady/kalmanjs/issues
- npm.io page: https://npm.io/package/kalmanjs-updated

## Alternatives

- [random-seedable](https://npm.io/package/random-seedable.md) — 27.9K weekly downloads
- [n2words](https://npm.io/package/n2words.md) — 22.2K weekly downloads
- [@stdlib/math-base-special-factorialln](https://npm.io/package/@stdlib/math-base-special-factorialln.md) — 5.7K weekly downloads
- [@stdlib/math-base-special-abs2](https://npm.io/package/@stdlib/math-base-special-abs2.md) — 1.7K weekly downloads
- [commons-math-interpolation](https://npm.io/package/commons-math-interpolation.md) — 1.4K weekly downloads

## Recent versions

- 1.0.1 (latest) — 2019-01-15

## README

# KalmanJS

Javascript based Kalman filter for 1D data. Sometimes you need a simple noise filter without any dependencies; for those cases *Kalman.js* is perfect.

![Kalman filter applied to a noisy dataset.](/resources/kalman-example.png?raw=true "Kalman filter applied to a noisy dataset.")

## Background

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/)

## Questions?

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.

### Node (es6)
`npm install kalmanjs`

```javascript
import KalmanFilter from 'kalmanjs';

const kf = new KalmanFilter();
kf.filter(2);
```

### Node (es5)
`npm install kalmanjs`

```javascript
var KalmanFilter = require('kalmanjs').default;

var kf = new KalmanFilter();
kf.filter(2);

```

## 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.

## Reference

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},}
```

## Other languages

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.

## Copyright

MIT License

Copyright (c) 2018 Wouter Bulten

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

---
_Source: https://npm.io/package/kalmanjs-updated · Machine-readable twin of the npm.io package page. Health data is recomputed on every publish._