Hacker News: alex_be

New comment by alex_be in "Leaving Mozilla"

alex_be — Sat, 13 Jun 2026 09:13:08 +0000

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

New comment by alex_be in "Pentagon raised threat of Israeli spying on U.S. to highest level, sources say"

alex_be — Sun, 07 Jun 2026 09:02:49 +0000

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 of trained terrorists coming out of tunnels and cross the fence through the openings. If you don't have prior intelligence, you can't stop it.

New comment by alex_be in "All 12 moonwalkers had "lunar hay fever" from dust smelling like gunpowder (2018)"

alex_be — Fri, 17 Apr 2026 22:08:00 +0000

"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

New comment by alex_be in "Intel 486 CPU announced April 10, 1989"

alex_be — Fri, 10 Apr 2026 16:23:49 +0000

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 years. Surprisingly, flat monitors appeared soon after...

New comment by alex_be in "Understanding the Kalman filter with a simple radar example"

alex_be — Fri, 10 Apr 2026 11:02:40 +0000

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.

New comment by alex_be in "Understanding the Kalman filter with a simple radar example"

alex_be — Thu, 09 Apr 2026 19:49:42 +0000

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.

New comment by alex_be in "Understanding the Kalman filter with a simple radar example"

alex_be — Thu, 09 Apr 2026 19:42:28 +0000

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

New comment by alex_be in "Understanding the Kalman filter with a simple radar example"

alex_be — Thu, 09 Apr 2026 15:18:22 +0000

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?

New comment by alex_be in "Understanding the Kalman filter with a simple radar example"

alex_be — Thu, 09 Apr 2026 15:08:16 +0000

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/

New comment by alex_be in "Understanding the Kalman filter with a simple radar example"

alex_be — Thu, 09 Apr 2026 14:55:05 +0000

Classic :)

New comment by alex_be in "Understanding the Kalman filter with a simple radar example"

alex_be — Thu, 09 Apr 2026 14:49:30 +0000

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.

New comment by alex_be in "Understanding the Kalman filter with a simple radar example"

alex_be — Thu, 09 Apr 2026 14:44:28 +0000

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 will consider a chapter on LQG.

Small clarification: nonlinear Kalman filters are suboptimal. EKF relies on linear approximations, and UKF uses heuristic approximations.

New comment by alex_be in "Understanding the Kalman filter with a simple radar example"

alex_be — Thu, 09 Apr 2026 14:32:02 +0000

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 hold for human witnesses.

New comment by alex_be in "Understanding the Kalman filter with a simple radar example"

alex_be — Thu, 09 Apr 2026 14:25:31 +0000

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

New comment by alex_be in "Understanding the Kalman filter with a simple radar example"

alex_be — Thu, 09 Apr 2026 05:43:24 +0000

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 to another."

So it is right to say that the implementation of the KF is tightly coupled to the system. Getting that part right is usually the hardest step.

New comment by alex_be in "Understanding the Kalman filter with a simple radar example"

alex_be — Wed, 08 Apr 2026 22:34:40 +0000

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

New comment by alex_be in "Understanding the Kalman filter with a simple radar example"

alex_be — Wed, 08 Apr 2026 21:26:13 +0000

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.

New comment by alex_be in "Understanding the Kalman filter with a simple radar example"

alex_be — Wed, 08 Apr 2026 19:52:39 +0000

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 repeated updates. However, the main strength of the Kalman filter is combining a model with noisy measurements, not necessarily relying on high sampling rates.

In practice, Kalman filters can work well even with relatively low-rate measurements, as long as the model captures the system dynamics reasonably well.

I also agree that it's often something you design into the system rather than applying as a post-processing step.

New comment by alex_be in "Understanding the Kalman filter with a simple radar example"

alex_be — Wed, 08 Apr 2026 19:48:54 +0000

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 design considerations, common pitfalls, and additional examples. But there are definitely many good free resources out there, including the one you linked.

New comment by alex_be in "Understanding the Kalman filter with a simple radar example"

alex_be — Wed, 08 Apr 2026 19:45:27 +0000

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 common pitfalls.