bayesclasses-general

[Bayes++] Estimate robot location from 5 independent estimates each with a varying uncertainty

[Martin N. <mar...@ho...>]

Hello everyone,I am currently doing a team project to build a robot. My rol=
e is to design the decision system. Currently I am working on estimating th=
e robot=92s location. I have inputs from about 5 different systems specifyi=
ng an estimate of the current location. Each of these will contain an (x,y)=
 coordinate and an uncertainty in pixels based upon a common map. The uncer=
tainty for each system varies depending upon where the robot is (e.g. when =
far away from a surveillance camera the uncertainty will be larger than whe=
n close to the camera). Some of these systems can also supply a rotation.I =
need to use a filter to combine these estimates into one with a new uncerta=
inty based upon the input uncertainties.I have read some of the past posts =
on this mailing list and was thinking about using the 'Addative_predict_mod=
el' in Bayes++. Would the noise be the uncertainty I am being given by the =
other systems? I was hoping someone could spend a couple of minutes and jus=
t point me in the right direction.Any help would be greatly appreciated.Tha=
nks,Martin
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[Bayes++] Accuracy of Kalman filter prediction

[Nithya N. V. <nvi...@cs...>]

Hi all,

I am using Bayes++ Covariance filter for prediction of interger/float 
values in a sequence. From general reading, I found that Kalman filter can 
say how good its prediction is. Can I find out how good my prediciton is 
from the Covariance filter? I currently follow the simpleExample.cpp for 
predict, update steps. Is there a function which I can invoke to get its 
prediction accuracy?


thanks,
Nithya

Re: [Bayes++] Accuracy of Kalman filter prediction

[Michael S. <ma...@mi...>]

Dear Nithya,

On Monday 07 May 2007 18:12, Nithya Nirmal Vijayakumar wrote:
> Hi all,
>
> I am using Bayes++ Covariance filter for prediction of interger/float
> values in a sequence. From general reading, I found that Kalman filter can
> say how good its prediction is. Can I find out how good my prediciton is
> from the Covariance filter?

The Coveriance filter is the standard implementation of the Kalman filter. 
That is it estimates the state vector (x) and a covariance matrix (X). The 
covariance is the estimate of how good the predicted/estimate state is.

> I currently follow the simpleExample.cpp for 
> predict, update steps. Is there a function which I can invoke to get its
> prediction accuracy?

The simpleExample is 1D position estimator. In this case there is only a 
single element in the covariance matrix, the variance of the position 
estimate. This is printed as  along with the state estimate at each step.

If you take the square root of the variance you have the standard deviation of 
the state estimate. The standard deviation is proportional to the 
uncertainity in the state. Therefore the smaller it is the more certain the 
prediction/estimate is.

Michael

-- 
___________________________________
Michael Stevens Systems Engineering

34128 Kassel, Germany
Phone/Fax: +49 561 5218038

Navigation Systems, Estimation  and
                 Bayesian Filtering
    http://bayesclasses.sf.net
___________________________________

Re: [Bayes++] Accuracy of Kalman filter prediction

[Nithya N. V. <nvi...@cs...>]

Thank you very much Michael. This info helps!

-Nithya


On Sat, 12 May 2007, Michael Stevens wrote:

> Date: Sat, 12 May 2007 19:23:42 +0200
> From: Michael Stevens <ma...@mi...>
> To: bay...@li...
> Subject: Re: [Bayes++] Accuracy of Kalman filter prediction
>
> Dear Nithya,
>
> On Monday 07 May 2007 18:12, Nithya Nirmal Vijayakumar wrote:
> > Hi all,
> >
> > I am using Bayes++ Covariance filter for prediction of interger/float
> > values in a sequence. From general reading, I found that Kalman filter can
> > say how good its prediction is. Can I find out how good my prediciton is
> > from the Covariance filter?
>
> The Coveriance filter is the standard implementation of the Kalman filter.
> That is it estimates the state vector (x) and a covariance matrix (X). The
> covariance is the estimate of how good the predicted/estimate state is.
>
> > I currently follow the simpleExample.cpp for
> > predict, update steps. Is there a function which I can invoke to get its
> > prediction accuracy?
>
> The simpleExample is 1D position estimator. In this case there is only a
> single element in the covariance matrix, the variance of the position
> estimate. This is printed as  along with the state estimate at each step.
>
> If you take the square root of the variance you have the standard deviation of
> the state estimate. The standard deviation is proportional to the
> uncertainity in the state. Therefore the smaller it is the more certain the
> prediction/estimate is.
>
> Michael
>
> --
> ___________________________________
> Michael Stevens Systems Engineering
>
> 34128 Kassel, Germany
> Phone/Fax: +49 561 5218038
>
> Navigation Systems, Estimation  and
>                  Bayesian Filtering
>     http://bayesclasses.sf.net
> ___________________________________
>
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