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

Yes, I'd echo thanks to parent, the OA and all still in the trenches.

Since 2007 in my case which is when I started using Linux at home. The distributions I use come with Firefox as the Web browser (Ubuntu, Debian and latterly Slackware).

I do find myself turning things off more now than I used to.

There were a few years where it was hard to justify using Firefox, it was just so slow compared to Chrome at the time. Nowadays it's fine again.

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.

"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

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...

Yeah but the first LCD screens sucked. Poor color rendition and not usable for gaming. In the early 2000s you were better off sticking with your CRT.

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.

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?

The map is the data model.

When doing Kalman filters, you usually have the basic form of the dynamics in the linear system, but the coefficients are usually determined experimentally (since things like mass is hard to estimate)

Additionaly, because i have direct integrator control (i.e when my target is at setpoint, my control input is 0), all I need is a proportional gain that is small enough for the system to not go unstable. And i have a physical low pass filter of the motor rotor inertia.

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 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.

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.

That's an interesting wrinkle. How would you model the potential bias in order to neutralize it though? Or would enough measurements simply cancel out any bias (or be very likely to)?