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Originally Posted by kearnel
I've been reading and trying to understand these algorithms but it appears that I need to understand calculus. I can't find anything that I can understand.
Edit: I'm wondering If there are any websites that explain filtering without using crazy-looking math to do it.
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You are correct that the types of filters that you will likely want to use do use calculus. They are typically introduced in upper division college classes, and covered in detail in graduate classes. Do you have any mentors with an electrical engineering or controls degree?
Filtering is very much dependent on the application. So far all you've told us is that you want to use the accelerometer for positioning, but you aren't using it for positioning. That makes it very hard to help. Filtering is a tradeoff between complexity (processing power), how well it keeps what you want, and how well it's able to reject what you don't want. Ether's filter is fairly simple filter that I've used before. It's very simple as far as processing, and is adjustable which is helpful. However it won't match the performance of a more complicated filter (which may or may not be necessary).
The advantage of using LabVIEW is the instant visualization of the data (besides the large number of built in filters). You can play around with the filters without understanding the math until you find a filter (and the coefficients) that work for your data. I would start with a Butterworth filter. Without the math background, all you'd be doing is copying code, so you'd be better off finding something already implemented. You can probably find a java implementation of a Butterworth filter.
For determining position with multiple sensors, a Kalman filter is probably what you really need, but that's several orders of magnitude harder then a Butterworth filter. I've seen several threads on chiefdelphi where people talked about them, but I'm not sure that anyone's successfully implemented one for a FIRST robot.