If you post the m file for kalman_filter, it'll be much easier to
help you, but here's my guess: You have a model
x(k+1)=Ax(k)+p(k)
y(k)=Cx(k)+m(k)
p(k) is process noise and m(k) is measurement noise. With this
model, the function inputs should be:
y: acceleration measurements
A: A matrix in the model
C: C matrix in the model
init_x: Initial position (x1(0) in the model)
init_v: Initial velocity (x2(0) in the model)
Q: Covariance matrix for p(k)
R: covariance matrix for m(k)
Hope this helps!
Omur
Sebastien Moindrot <smoindrot@aol.com> wrote in message
news:wehj160yitxf@forum.mathforum.com...
> Hi there,
>
> I have little knowledge with Kalman filters, but I think I understand
> the abouts of it. I have accelerometers measuring raw data and I
> would like to use a Kalman filter to get a better data to integrate
> for the velocity and then the position.
> I have seen some codes for filters but I don't really understand the
> inputs:
> [x, V, VV, loglik] = kalman_filter(y, A, C, Q, R, init_x, init_V,
> model).(http://www.cs.berkeley.edu/~murphyk/Bayes/kalman.html)
> I have a vector acceleration, time sample (or time vector) and this is
> it.
> Could you please help me.
>
> Cheers,
> Sebastien
