https://github.com/callumjhays/elm-kalman-filter

Simple 1D Kalman Filters in Elm
https://github.com/callumjhays/elm-kalman-filter

elm elm-lang kalman kalman-filter kalman-filtering signal-processing signals

Last synced: about 1 month ago
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Simple 1D Kalman Filters in Elm

• Host: GitHub
• URL: https://github.com/callumjhays/elm-kalman-filter
• Owner: CallumJHays
• License: mit
• Created: 2017-04-26T01:37:50.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2017-04-28T16:14:50.000Z (over 7 years ago)
• Last Synced: 2024-11-21T00:59:30.750Z (about 1 month ago)
• Topics: elm, elm-lang, kalman, kalman-filter, kalman-filtering, signal-processing, signals
• Language: Elm
• Homepage: http://package.elm-lang.org/packages/CallumJHays/elm-kalman-filter/2.0.2
• Size: 57.6 KB
• Stars: 2
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# elm-kalman-filter

Simple 1D Kalman filters in Elm

![kalman filter graph](https://raw.githubusercontent.com/CallumJHays/elm-kalman-filter/master/example.png "Example of a Kalman Filter acting on a noisy quadratic signal")

## Installation 

```bash

elm package install CallumJHays/elm-kalman-filter

```

## Example

```elm

-- `model.noise` contains 100 gaussian-random numbers generated by

-- elm-community/random-extra: `Random.Float.standardNormal`

xAxis =

    List.Range 0 1000

-- Apply a quadratic function

signal =

    xAxis |> List.map (\x -> 0.001 * (x - 10) ^ 2 - (x - 10))

noisySignal =

    signal |> List.map2 (+) model.noise

predictedSignal : List Float

predictedSignal =

    KalmanFilter.filter Nothing noisySignal

```

## Getting Started

The `filter` functon is the easiest way to use a Kalman Filter:

```elm

predictedSignal : List Float

predictedSignal =

    KalmanFilter.filter Nothing noisySignal

```

However, it might not be the most appropriate for your use case. For example,

if your application recieves rolling updates of a signal from an API server

that you need filtered, it would be more appropriate to keep a copy of

`KalmanFilter.Model` in your application Model, and to use it along with

`KalmanFilter.filterMeasurement` to provide less noisy signals as they come in.

One great concrete use-case for usage with an API server is multiplayer video-

games that require a mechanism for preventing [Rubber-banding](https://en.wikipedia.org/wiki/Rubber_banding). The Kalman filter may be used

without smoothing to observe the values being passed in (using `filter` with

the `Param.expectedNoisePower` parameter set to `0`). Backup values can then be

provided in durations when the network lags using `predictNext`. Of course,

this use-case would be far more relevant if this library was generalised to

N-Dimensional data (2D, 3D) - but that's still a work in progress :)