Viewing profile — alex_be

I've been using Firefox for almost 20 years as my default browser. Thank you for your work!

I've been and I've seen. It is a fence. A very long fence. You can't cross it with bare hands, and if you try, you will be shot. This is not the case when dozens of bulldozers simu…

"In addition the Moon has no atmosphere and is constantly bombarded by radiation from the Sun that causes the soil to become electrostatically charged." - You can use a magnetic or…

486 was my dream. Unfortunately, my parents didn't have money for it. I bought my first PC in 1999 - a Pentium 2. I invested a lot of money in the monitor; computers become obsolet…

Thanks a lot for your detailed and valuable comments. I will definitely include them in the tutorial. If you have additional comments, I would be happy to hear them.

True. It's about managing the risk rather than eliminating it. If you remove an outlier, you get a missing measurement and, as a result, higher uncertainty (error). But it is still…

Thanks a lot for this comment, Ted! This probably deserves its own example, not just a brief mention. I will definitely do that.

Interesting. It sounds like you ended up with a data-driven estimator. Did you have a chance to compare the data-driven and model-based approaches?

It's a free accessibility widget by Sienna. I tweaked the CSS to adapt it to the https://kalmanfilter.net/ style. You can find it here: https://accessibility-widget.pages.dev/

Classic :)

I have a chapter in my book that introduces sensor fusion as a concept. If you want to dive deeper into the sensor fusion topic, I would recommend Bar-Shalom's or Blackman's book.

Thanks for your feedback. I am thinking of writing a second volume with more advanced and less introductory topics, but I haven't decided yet. It is a serious commitment and it wil…

Kalman filter is about combining uncertain measurements, and human observations could be viewed as noisy sensors. On the other hand, the standard KF assumes unbiased sensors with G…

Yeah. Building things step by step often makes complex topics much easier to understand.

Thanks for your feedback! Actually the KF concept is generic, but as mentioned above: "The state transition and measurement equations belong to the system model. They describe the …

It is always a good idea to include outliers treatment in KF algorithm to filter out weird measurements.

The tutorial actually predates ChatGPT by quite a few years (first published in 2017). Today, I do sometimes use ChatGPT to fix grammar, but I am responsible for the content and it…

I agree that Kalman filters are not magic and that having a reasonable model is essential for good performance. Higher sampling rates can help in some cases, especially when tracki…

That's a fair question. My goal with the site was to make as much material available for free as possible, and the core linear Kalman filter content is indeed freely accessible. Th…

That's a good article. I also like the visual approach there. My goal here was a bit different. I walk through a concrete radar example step by step, and use multiple examples thro…

That's an interesting idea. The Kalman filter is definitely used in finance, often together with time-series models like ARMA. I've been thinking about writing something, although …

You’re pointing out a real conceptual issue: where the system model ends and where the Kalman filter begins. In Kalman filter theory there are two different components: - The syste…

That's a good point. "Optimal" in this context means that, under the standard assumptions (linear system, Gaussian noise, correct model), the Kalman Filter minimizes the estimation…

To keep the example focused and reasonably short, I treated Q matrix as given and concentrated on building intuition around prediction and update. But you're right that this can fe…

Author here. I recently updated the homepage of my Kalman Filter tutorial with a new example based on a simple radar tracking problem. The goal was to make the Kalman Filter unders…