https://github.com/xylambda/kalmankit

Multidimensional implementation of standard and extended Kalman Filters. Filtering and smoothing.
https://github.com/xylambda/kalmankit

extended-kalman-filter filter kalman kalman-filter kalman-smoother numpy python rts-smoother signal-extraction signal-processing smoother

Last synced: about 10 hours ago
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Multidimensional implementation of standard and extended Kalman Filters. Filtering and smoothing.

• Host: GitHub
• URL: https://github.com/xylambda/kalmankit
• Owner: Xylambda
• License: apache-2.0
• Created: 2020-05-17T18:00:19.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2026-06-05T22:17:47.000Z (25 days ago)
• Last Synced: 2026-06-30T13:00:47.051Z (about 10 hours ago)
• Topics: extended-kalman-filter, filter, kalman, kalman-filter, kalman-smoother, numpy, python, rts-smoother, signal-extraction, signal-processing, smoother
• Language: Python
• Homepage: https://xylambda.github.io/kalmankit/
• Size: 3.21 MB
• Stars: 17
• Watchers: 1
• Forks: 4
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE

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The Kalman filter is an optimal estimation algorithm: it estimates the true 

state of a signal given that this signal is noisy and/or incomplete. This 

package provides a multidimensional implementation of:

* **Standard Kalman Filter**: if the noises are drawn from a gaussian 

distribution and the underlying system is governed by linear equations, the 

filter will output the best possible estimate of the signal's true state.

* **Extended Kalman Filter**: can deal with nonlinear systems, but it does not

guarantee the optimal estimate. It works by linearizing the function locally

using the Jacobian matrix.

## Installation

**Normal user**

```bash

pip install kalmankit

```

**Developer**

```bash

git clone https://github.com/Xylambda/kalmankit.git

pip install -e "kalmankit/.[dev]"

```

## Tests

To run tests you must install the library as a `developer`.

```bash

cd kalmankit/

pytest -v tests/

```

## Usage

The library provides 5 examples of usage:

1. [Moving Average](examples/moving_average.py)

2. [Market Beta estimation](examples/market_beta.py)

3. [Pendulum estimation with EKF](examples/pendulum.py)

4. [Standard RTS smoothing for constant-velocity tracking](examples/constant_velocity_smoothing.py)

5. [Extended RTS smoothing for a nonlinear pendulum](examples/pendulum_smoothing.py)

Install the `examples` extra to run the examples:

```bash

pip install -e ".[examples]"

```

## References

* Matlab - [Understanding Kalman Filters](https://www.youtube.com/playlist?list=PLn8PRpmsu08pzi6EMiYnR-076Mh-q3tWr)

* Bilgin's Blog - [Kalman filter for dummies](http://bilgin.esme.org/BitsAndBytes/KalmanFilterforDummies)

* Greg Welch, Gary Bishop - [An Introduction to the Kalman Filter](https://www.cs.unc.edu/~welch/media/pdf/kalman_intro.pdf)

* Simo Särkkä - Bayesian filtering and Smoothing. Cambridge University Press.

## Cite

If you've used this library for your projects please cite it:

```latex

@misc{alejandro2021kalmankit,

  title={kalmankit - Multidimensional implementation of Kalman Filter algorithms},

  author={Alejandro Pérez-Sanjuán},

  year={2021},

  howpublished={\url{https://github.com/Xylambda/kalmankit}},

}

```