On 3/9/20 2:36 PM, Poul-Henning Kamp wrote:
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In message <3899483.rfyw6ut...@linux-5fgm.suse>, Matthias Welwarsky writes:
I've actually been thinking of using a Kalman filter to find the "true value"
of the EFC. I just couldn't wrap my mind around the theory yet.
Yeah, they are hard to get started with, there seems to be only two
kinds of texts about Kalman: Hard-core math and woo-doo library usage.
Kalman filters are actually pretty simple..
They're basically a single exponential type smoothing filter y(i) =
alpha * x(i) + (1-alpha)*y(i-1)
where you choose alpha to be related to the current uncertainty of the
estimate and the uncertainty of the measurement, so that each
contributes such that the new estimate has the minimum uncertainty.
Where it gets tricky is when you have multiple variables in and out, and
you need to have the covariances of the inputs to be able to "choose wisely"
And, since in most implementations, the multiple variables are the state
variables (x(t), x'(t), x''(t), etc). the uncertainty in the
measurements of the higher derivatives tends to be higher (because a
differentiator is a high pass filter).
A Maximum Likelihood LMS estimator is essentially a special case of a
Kalman filter.
And, of course, because no modern discussion is complete without working
in Machine Learning: the classic LMS adjustment methods(w(i+1) = w(i) +
alpha * x(i)) for a single layer classifier boils down to the same thing.
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