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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.
Kalman filters are very cool, but when applying them you've got to know that they're not magic. I struggled to apply Kalman Filters for a toy project about ten years ago, because the thing I didn't internalize is that Kalman filters excel at offsetting low-quality data by sampling at a higher rate. You can "retroactively" apply a Kalman filter to a dataset and see some improvement, but you'll only get amazing results…
All of that stuff is used in industry because a lot of regulation (for things like aircraft) basically requires your control laws to be linear so that you can prove stability.
In reality, when you get into non linear control, you can do a lot more stuff. I did a research project in college where we had an autonomous underwater glider that could only get gps lock when it surfaced, and had to rely on shitty MEMS imu control under water. I actually proposed doing a neural network for control, but it got shot down because "neural nets are black boxes" lol.
I really loved this one: https://www.bzarg.com/p/how-a-kalman-filter-works-in-picture...
I feel like people overcomplicate even the "simple" explanations like the OPs and this one. Basically, a Kalman filter is part of a larger class of "estimators", which take the input data, and run additional processing on top of it to figure out the true measurement. The very basic estimator a low pass filter is also an "estimator" - it rejects high frequency noise, and gives you essentially a moving average. But is…
This seems to be an ad for a fairly expensive book on a topic that is described in detail in many (free) resources. See for example: https://rlabbe.github.io/Kalman-and-Bayesian-Filters-in-Pyth... Is there something in this particular resource that makes it worth buying?
This seems to be an ad for a fairly expensive book on a topic that is described in detail in many (free) resources. See for example: https://rlabbe.github.io/Kalman-and-Bayesian-Filters-in-Pyth... Is there something in this particular resource that makes it worth buying?
There are not many good resources on Kalman filters. In fact, I have found a single one that I'd consider good. This is someone who has spent a lot of time to newly understand Kalman filters.
Earlier quoted context omitted.
There are not many good resources on Kalman filters. In fact, I have found a single one that I'd consider good. This is someone who has spent a lot of time to newly understand Kalman filters.
Link to that good one?
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 linear algebra, without requiring advanced mathematics. The example starts with a radar measuring the distance to a moving object and gradually builds intuition around noisy measurem…
I really loved this one: https://www.bzarg.com/p/how-a-kalman-filter-works-in-picture...
I feel like people overcomplicate even the "simple" explanations like the OPs and this one. Basically, a Kalman filter is part of a larger class of "estimators", which take the input data, and run additional processing on top of it to figure out the true measurement. The very basic estimator a low pass filter is also an "estimator" - it rejects high frequency noise, and gives you essentially a moving average. But is…
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 linear algebra, without requiring advanced mathematics. The example starts with a radar measuring the distance to a moving object and gradually builds intuition around noisy measurem…