alex_be

7 ms·

1.
by alex_be 3mo ago
I've been using Firefox for almost 20 years as my default browser. Thank you for your work!
2.
by alex_be 3mo ago
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 simultaneously cross the fence and many thousands
3.
by alex_be 5mo ago
"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 electric field to push the soil away
4.
by alex_be 5mo ago
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 obsolete very quickly, while a monitor can serve for many year
5.
by alex_be 5mo ago
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.
6.
by alex_be 5mo ago
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 better than keeping the outlier.
7.
by alex_be 5mo ago
Thanks a lot for this comment, Ted! This probably deserves its own example, not just a brief mention. I will definitely do that.
8.
by alex_be 5mo ago
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?
9.
by alex_be 5mo ago
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/
10.
by alex_be 5mo ago
Classic :)
11.
by alex_be 5mo ago
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.
12.
by alex_be 5mo ago
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 will take years to complete. If I take this decision, I wi
13.
by alex_be 5mo ago
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 Gaussian noise, and I don't know if those assumptions ho
14.
by alex_be 5mo ago
Yeah. Building things step by step often makes complex topics much easier to understand.
15.
by alex_be 5mo ago
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 physics of the system and can vary from one application
16.
by alex_be 5mo ago
It is always a good idea to include outliers treatment in KF algorithm to filter out weird measurements.
17.
by alex_be 5mo ago
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 is always mine.
18.
by alex_be 5mo ago
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 tracking fast dynamics or reducing measurement noise through repea
19.
by alex_be 5mo ago
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. The book goes further into topics like tuning, practical
20.
by alex_be 5mo ago
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 throughout the tutorial to build intuition and highlight co
21.
by alex_be 5mo ago
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 it's a bit outside my usual engineering focus
22.
by alex_be 5mo ago
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 system model - The Kalman filter (the algorithm) The state transi
23.
by alex_be 5mo ago
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 error covariance. In other words, it provide
24.
by alex_be 5mo ago
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 feel like it appears out of nowhere. The derivation of th
25.
by alex_be 5mo ago
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 understandable to anyone with basic knowledge of statistics and li
26.
Understanding the Kalman filter with a simple radar example(kalmanfilter.net)
433 points by alex_be 5mo ago|66 comments
27.
by alex_be 6mo ago
Big step toward AI-assisted mathematical research
28.
by alex_be 7mo ago
Very interesting and so actual. Isaac Asimov is one of my favorite writers.
29.
by alex_be 3y ago
Hello mojomark, My name is Alex Becker, and I am the author of the "Kalman Filter from the Ground Up book." First, I would like to thank you for your feedback. As an author, it is important for me to receive feedback from the read