Tutorial repository for the paper submitted at FUSION 2025 with title: "Stone Soup goes NUTS: adding proposals and The No-U-Turn Sampler to Stone Soup"
Particle filters are essential for state estimation in non-linear and non-Gaussian systems, with performance hinging on effective proposal distributions. This paper presents the implementation of Kalman Filter and No-U-Turn Sampler (NUTS) proposals within the Stone Soup Python framework. The Kalman Filter proposal offers computational efficiency for structured systems, while NUTS enables robust exploration of complex, high-dimensional distributions. Benchmark evaluations demonstrate the complementary strengths of these methods, enhancing Stone Soup’s capabilities for diverse state estimation challenges
Alberto Acuto1, Lyudmil Vladimirov1, Alessandro Varsi1, Paul Horridge1 and Simon Maskell1
1 University of Liverpool, Department of Electrical Engineering and Electronics
The code shared can be run and modifief for various uses. We refer to the Stone Soup community for further examples on how to use the framework, raise issues on the implementation and proposing and implementing improvements.
To install and test the code provided you can use a virtual environment. How to set up a virtual environemnt:
python3 -m venv <name_of_venv>
source <name_of_venv>/bin/activate
.\<name_of_venv>\Scripts\activate
Then you can install all the dependencies by doing:
python -m pip install -e .[dev]
This code is built on top of Stone Soup and when the code will be reviewed and included in the main branch we will update the installation guide for easier running of the tutorial.
Coming soon
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