extended kalman filter going berserk

On Tue, 04 Aug 2009 07:16:09 -0500, hagai_sela wrote:

> Hi guys,
> I ran some tests, eigenvalues are positive at all times. It seems that
> the problem is caused by a specific measurement which is 27 standard
> deviations more than the average (The network fails at this point). I
> tried taking the natural log of the data instead but the results seem to
> be worse... any ideas?

Toss that measurement out.  Kalman filters are optimal for Gaussian data, 
whose values exceed a few standard deviations very rarely indeed.  "Long 
tail" distributions are much more common in the real world, i.e. data 
that is mostly Gaussian with a few "outliers"; with such distributions 
the best technique is often to ignore those outliers.

It's up to you to decide if it's worthwhile or wise to try to toss out 
just one measurement or the surrounding ones, as well -- if one really 
bad measurement is an indication that it's neighbors are bad, too, then 
you may want to cut out a group of them.

-- 
www.wescottdesign.com

bump.

Still having problems...
I discovered that the reason for the high values is that the covariance
matrix goes to almost zero. How can I prevent this?

Hagai.

On Mon, 04 Jan 2010 08:05:39 -0600, "hagai_sela"
<hagai.sela@gmail.com> wrote:

>bump.
>
>Still having problems...
>I discovered that the reason for the high values is that the covariance
>matrix goes to almost zero. How can I prevent this?

Did you increase your process noise? 
-- 
Muzaffer Kal

DSPIA INC.
ASIC/FPGA Design Services

http://www.dspia.com

I am using the values in Haykin's book (kalman filtering and neural
networks). I am using diagonal matrices for the process and measurement
noise as he specified, I tried increasing the values on the diagonal, and
now the covariance matrix is not zero, but it has low values on
off-diagonal items and high values in the diagonal items. The network's
output is still very high.
Any pointers on what I should look for?

Hagai.