On Jul 21, 7:55�pm, d...@myallit.com wrote:
> On Jul 22, 12:44&#4294967295;am, Chris Maryan <kmar...@gmail.com> wrote:
>
> > On Jul 20, 7:03&#4294967295;am, "ac123" <d...@myallit.com> wrote:
>
> > > Any ideas?
>
> > The missing measurements\uneven sampling rate is usually a matter of
> > being able to express your state transition matrix as a function of
> > the sampling time. This is easiest to understand when you are
> > filtering, a bit more complicated when you are predicting.
>
> > Chris
>
> So if my state transition is given by:
>
> F = [dt 1
> &#4294967295; &#4294967295; &#4294967295;1 &#4294967295;0]
>
> where dt is the time step, then you mean I just alter dt at each step?
> Do I also need to alter the measurement matrix to account for the
> missing measurements?

If this is a typical state-space system, then the observation equation
should be independent of the time step.

On Jul 22, 12:44&#4294967295;am, Chris Maryan <kmar...@gmail.com> wrote:
> On Jul 20, 7:03&#4294967295;am, "ac123" <d...@myallit.com> wrote:
>
> > Any ideas?
>
> The missing measurements\uneven sampling rate is usually a matter of
> being able to express your state transition matrix as a function of
> the sampling time. This is easiest to understand when you are
> filtering, a bit more complicated when you are predicting.
>
> Chris

So if my state transition is given by:

F = [dt 1
     1  0]

where dt is the time step, then you mean I just alter dt at each step?
Do I also need to alter the measurement matrix to account for the
missing measurements?

On Jul 20, 7:03&#4294967295;am, "ac123" <d...@myallit.com> wrote:
> Any ideas?

The missing measurements\uneven sampling rate is usually a matter of
being able to express your state transition matrix as a function of
the sampling time. This is easiest to understand when you are
filtering, a bit more complicated when you are predicting.

Chris

Any ideas?

I'm trying to implement a Kalman filter in MATLAB that will use two types
of measurements: volume and in/out flow rate. For the flow rate, the
measurement error is additive Gaussian, but for the volume the measurement
error is expressed as a percentage of the volume, so that the volume
measurement is less accurate when its value is higher. I think the
measurement model should therefore be:

Flow rate measurement model:
z1 = x1 + v1 where v1 ~ N(0,e1)

Volume measurement model:
z2 = x2*v2 where v2 ~ N(1,e2)

I assumed the volume filtering should be done in the log domain to make
the noise additive but how do I deal with a noise mean of one when the
Kalman filter assumes a mean of zero? And how can I have a Kalman filter
using both the measurements if one is in the log domain and the other one
isn't?

I am also dealing with a system where measurements will usually be missing
(they are arriving sequentially) and at an uneven sampling rate, any other
pointers on these too would be appreciated.