Quote:
Originally Posted by GuyM142
do you have some information about filters which I can start with?
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"Filter" is a general term meaning a system which does some operation on a signal. They encompass a whole sub-field of (usually) electrical engineering called signal processing, so the best I can do is point you to
an introductory book to get you started, if that's what you're interested in.
Regarding complementary and Kalman filters specifically, there's a rather good
white paper (
pdf version) posted to CD a few years ago on complementary filters, that also includes most of what I posted above.
I suggest you start here.
You can also get several good webpages that address both complementary filters and Kalman filters by
Googling on complementary filters.
To quote from the white paper, though, "[Kalman filters are] mathematically complex, requiring some knowledge of linear algebra." Understanding the modern derivation of them also requires a basic understanding of multivariate probability. Assuming you're still in high school, these may be a little out of reach for you until you've taken some college courses. However, there's a
fantastic online course on Udacity called "Artificial Intelligence for Robotics" that includes a lesson on Kalman filters. If you're a good math student and are feeling adventurous, try it out - it does a good job of breaking down the algorithm so it's more accessible (much more so than trying to figure out the raw equations on the
Wikipedia page).
If you keep doing robotics after high school, check out
Probabilistic Robotics by Thrun (who also teaches the online course I linked), Burgard, and Fox after you've gotten some college math courses under your belt. It's a great overview of techniques for state estimation in robotics from some of the
leaders in
the field.