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Kalmanformer

Kalmanformer is a Python library that uses a transformer to learn the Kalman gain from observations and estimate post-update states across a sequence, including in nonlinear-system settings.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Kalmanformer — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when your Python workflow needs sequence-based state estimates and you can supply observations, state-transition and observation matrices, and an initial state.

Who it is for

  • Developers building state-estimation or tracking components.
  • Researchers exploring transformer-based Kalman filtering.
  • Robotics and sensor application teams that can provide observations and system matrices.

Documented uses

  • Estimate a sequence of post-update states from observations.
  • Explore learned Kalman-gain estimation for nonlinear-system observations.
  • Use estimated states as a tracking component in a larger application.
Before you choose

Requirements & limits

The repository is public and lists an MIT license. Installation is documented through pip.

  • The example expects state and observation dimensions along with F, H and an initial state.
  • The documented call returns post-state estimates for each observation in the sequence.
  • The README's sample inputs are mock random data, not evidence of performance on a particular dataset or system.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install the library and prepare the documented example

Set up the package and construct a KalmanFormer call using the README's example inputs.

Before you start

  • Python environment with pip
  • PyTorch
  • Observation tensors, state-transition matrix F, observation matrix H and initial state x_0

Steps

  1. Install the package with the command below.

    Documented command
    pip install kalmanformer

  2. Import torch and KalmanFormer.

  3. Create a KalmanFormer instance with state and observation dimensions and transformer settings.

  4. Provide observations, F, H and x_0 to the model call.

Expected outcome

The documented example returns post-state estimates with shape (2, 10, 3) for its sample dimensions.

Guide limitations

  • The README uses randomly generated example tensors rather than real observations.
  • The example does not document training, data preparation or integration into a downstream application.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
lucidrains/kalmanformer · Official homepage title · retained in its source language
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