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baggepinnen / LowLevelParticleFilters.jl

State estimation, smoothing and parameter estimation using Kalman and particle filters.
https://baggepinnen.github.io/LowLevelParticleFilters.jl/stable
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Improve documentation #130

Closed ufechner7 closed 9 months ago

ufechner7 commented 9 months ago

There is an "advanced tutorial" section in the manual, but I miss a "basic tutorial".

I could add the example we discussed here https://discourse.julialang.org/t/discrete-kalman-filter/109014 as pull request to the documentation, also extend it a bit as an example for amending a dynamical system to estimate an additional state (Ta)...

What do you think? And if you think this is a good idea, where in the documentation should I add it?

baggepinnen commented 9 months ago

The index page is the basic tutorial. Our discussions in the discourse thread were not really related to the use of this package, they were related to fundamental understanding of how a Kalman filter works and how to model stochastic dynamics, which I assume that a user of this package should already know and is therefore outside of the scope for the documentation of this package. I recommend the book "Statistical Sensor Fusion" by Fredrik Gustafsson.

You might also find this blog post interesting: https://info.juliahub.com/tune-kalman-filter also available as a Pluto notebook https://juliahub.com/pluto/editor.html?id=ad9ecbf9-bf83-45e7-bbe8-d2e5194f2240

baggepinnen commented 9 months ago

I added some links to this tutorial in the docs

ufechner7 commented 9 months ago

Very nice tutorial, thanks for sharing!

It took me some time to understand that obs and nonlin are footnotes in the blog post, the square brackets are missing.

baggepinnen commented 9 months ago

yeah, the formatting in the notebook unfortunately got a bit butchered when the notebook was translated to a web page :(