implement square root extended Kalman filter
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help wanted
- Dominant language
- Python
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- 1k
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- 114
- Avg merge
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- Merged PRs (30d)
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Description
See also this existing JAX implementation https://github.com/EEA-sensors/sqrt-parallel-smoothers.
It should be easy to port this to https://github.com/probml/dynamax/blob/main/dynamax/nonlinear_gaussian_ssm/inference_ekf.py.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading dynamax/nonlinear_gaussian_ssm/inference_ekf.py and comparing its EKF flow with the existing JAX implementation in EEA-sensors/sqrt-parallel-smoothers. Determine the corresponding square-root filter behavior and verify that the port integrates with the existing inference entry points; done means the square-root extended Kalman filter is implemented there.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100