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The Kalman Filter

How Neil Armstrong went to the Moon…and returned

The Kalman Filter is one the most important algorithms for tracking and estimation of parameters in a dynamical system.
Published

July 1, 2026

Modified

November 7, 2026

Halumi

Halumi

Intro

Self-Driving Cars as an example of a Dynamic System

Idea of State Space

Halumi for Dinner?

Shubha’s Halumi with a kebab stick to illustrate 3D Gaussian Distributions for prediction and measurement in a Kalman system.

References

  1. bzarg blog. How a Kalman Filter Works, in Pictures. https://www.bzarg.com/p/how-a-kalman-filter-works-in-pictures/

  2. Ramsey Faragher. Understanding the Basis of the Kalman Filter Via a Simple and Intuitive Derivation.

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  3. Tim Babb.The Kalman Filter in Pictures.https://www.bzarg.com/p/how-a-kalman-filter-works-in-pictures/

  4. The Kalman Filter Website for Students. https://thekalmanfilter.com/

  5. Chapter 6. The Kalman Filter. https://bookdown.org/probability/bookdown-demo/kalman-filters.html in Stochastic Processes! An interactive introduction.

  6. Kalman Filter in Julia. https://github.com/wkearn/Kalman.jl

  7. Chuong B. Do. (October 10, 2008). The Multivariate Gaussian Distribution. https://cs229.stanford.edu/section/gaussians.pdf

  8. https://ccrma.stanford.edu/~jos/filters/

Additional Reading

  1. Ramsey Faragher https://sites.google.com/view/ramseyfaragher/papers
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