Re: clutter modeling techniques
- From: beliavsky@xxxxxxx
- Date: 21 Jul 2005 09:09:09 -0700
vizziee@xxxxxxxxx wrote:
> Hi.
>
> I am a newbie in stats and mostly work in digital electronics. However,
> one of my recent jobs is clutter modeling, which may need some guidance
> from stat people. Clutter modeling is the term used in radar/sonar
> technology. It requires finding out the distribution (with a closed
> form expression, of course) that best fits the given raw/scatter data.
> This information is useful in determining some signal processing
> options that can give best results, should the distribution is known.
What you call "clutter modeling" sounds like "density estimation" to a
statistician.
If you know the functional form of the distribution, such as Gaussian
or Student t, you can use maximum likelihood estimation (MLE) to
estimate the distribution parameters from the data. Sometimes there are
analytical estimates, otherwise you can use an optimizer. This is
discussed in statistics textbooks.
> I read some literature on the topic about the techniques that can be
> used here. Perhaps probability paper plot, chi-square test and KS test
> are the ones (since they match two distributions; however in this case
> we need to assume a distribution for our raw data.....is this the
> correct method?).
Once you have fit a distribution via MLE, you can check the
goodness-of-fit using the tests you mentioned. If the fit is poor, you
may need to consider a broader class of distributions.
.
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