adaptive kalman filtering with dynamics update?

On Mon, 14 Nov 2011 18:36:56 -0600, doublehelics wrote:

> Yes, I was looking at MMSE and RLS solutions because they tend to have
> different convergence characteristics.
> 
> I think I need to learn Kalman filtering theory in more depth to see
> what can be done and what cannot, but my main problem is that the signal
> that we are trying to model (coming from some biological experiment) is
> not studied very well and I am not even sure about how to define the
> noise in the signal, let alone how to estimate the noise levels. maybe i
> could estimate the noise level (based on some definition) on the fly??
> again, maybe not. my biology friends should be of help there, I hope.

"Optimal State Estimation" by Simon.  I don't know if it's the very best 
book on Kalman filtering, but it was certainly good enough for me.

Keep in mind that the Kalman filter is just smack-your-forehead obvious 
if you already know probability and statistics, which I already had a 
pretty good grasp of going in.  So I mostly read that books for 
techniques and to have pitfalls pointed out.

-- 
www.wescottdesign.com

On Nov 15, 12:09&#4294967295;pm, Tim Wescott <t...@seemywebsite.com> wrote:
> On Mon, 14 Nov 2011 15:15:41 -0600, doublehelics wrote:
> > Thanks for the info, I agree that my post sounds a little vague, may be
> > i should have been more clear about what I was trying to ask.
>
> > I was merely trying to ask if there is a way to do adaptive predictive
> > filtering using Kalman's approach (I already looked at adaptive mmse and
> > rls filtering techniques.) I was just researching whether Kalman
> > filtering would be applicable and would add anything new to what i am
> > doing. What I meant by I do not know the system dynamics is basically we
> > do not have an initial model of how system is behaving, BUT we want to
> > use linear predictive model. From this point of view, it seems to me
> > like this is more of a system identification problem.
>
> > From your post I understand that there is a way to do this but its very
> > similar to other adaptive filtering methods.
>
> RLS and MMSE are both solving the same problem as a Kalman filter, so you
> would expect them to get the same answer.
>
> I'm pretty sure that they're basically specialized extended Kalman
> filters -- if you had the input to a system and the output and tried to
> find the optimal system identification in the least-squares sense then
> your answer wouldn't approximate a Kalman filter -- it _would be_ a Kalman
> filter.
>
> If you have reason to suspect that the probabilities involved are far
> from being Gaussian, or if your costs are far from being error-squared,
> then "none of the above" may prove to be the best answer.
>
> --www.wescottdesign.com

RLS is deterministic, a kalman filter is not. there rae many papers
trying to do what you do. Most of them use an extended kalman filter
and use an AR model for the speech. Some use H infinity filters to do
a similar type thing.


Hardy

On Nov 15, 3:38&#4294967295;am, Vladimir Vassilevsky <nos...@nowhere.com> wrote:
> doublehelics wrote:
> > Hey all, I have a quick question about Kalman filtering: I have been
> > reading about it and I need some opinions about whether Kalman filters are
> > suitable for my problem at hand: I need to do predictive filtering on a
> > non-stationary digital signal. There is no noise in the system. Also, I do
> > not have a dynamic model for the signal, either. On top of that, any
> > dynamic model that I assume will not be constant since the signal is not
> > stationary. So I am thinking that the Kalman filter that will be used
> > requires an adaptive correction on the dynamical model it uses (I could not
> > find any papers on correcting dynamical model.) The reason why I am
> > assuming Kalman filters may be helpful in this problem is that they are
> > used for tracking, and in my case my problem is basically tracking a
> > signal. So does anyone have any thoughts on whether Kalman filtering would
> > be applicable to this case? If you could direct me to papers/resources that
> > would be great. Thanks much in advance.
>
> > Oz.

are you 10 years old?

On Nov 15, 3:31&#4294967295;am, HardySpicer <gyansor...@gmail.com> wrote:
> On Nov 15, 3:38&#4294967295;am, Vladimir Vassilevsky <nos...@nowhere.com> wrote:
>
>
>
>
>
>
>
>
>
> > doublehelics wrote:
> > > Hey all, I have a quick question about Kalman filtering: I have been
> > > reading about it and I need some opinions about whether Kalman filters are
> > > suitable for my problem at hand: I need to do predictive filtering on a
> > > non-stationary digital signal. There is no noise in the system. Also, I do
> > > not have a dynamic model for the signal, either. On top of that, any
> > > dynamic model that I assume will not be constant since the signal is not
> > > stationary. So I am thinking that the Kalman filter that will be used
> > > requires an adaptive correction on the dynamical model it uses (I could not
> > > find any papers on correcting dynamical model.) The reason why I am
> > > assuming Kalman filters may be helpful in this problem is that they are
> > > used for tracking, and in my case my problem is basically tracking a
> > > signal. So does anyone have any thoughts on whether Kalman filtering would
> > > be applicable to this case? If you could direct me to papers/resources that
> > > would be great. Thanks much in advance.
>
> > > Oz.
>
> are you 10 years old?