Best resampling approach for different types of data?

On 3 Sep, 18:12, Tim Wescott <t...@seemywebsite.com> wrote:

> I'm not sure what restrictions Rune is placing by saying "not quite
> DSP".=A0If it's a signal, and you're processing it, and you're doing it
> numerically, then it's DSP to me.

What we usually think of as DSP is only a subset of the
methods available for data analysis. Kalman filters may
be bordeline, but tools like ARIMA analysis, median
filters and so on, are not covered by DSP.

You don't need to go further than the 'academic sibling'
of DSP, image processing, to see that one uses methods
like morphology, that have no counterpart in DSP.

As for the data, DSP usually covers anthropogenic data:
The devices listen for signals that somebody deliberately
emitted, be it for radar/sonar type of remote sensing,
or for communication purposes. There are exceptions, like
SETI or earthquake monitoring, but even these types of
applications listen for the same *types* of data; data
that propagate as waves into a world where they may or
may not be expetcted to exist.

To see what I mean, try to use, say, a Butterworth filter
to clean up navigation data, the noisy position estimates
from some vehicle that follows a meandering track. These
types of data have fundamentally different characteristics
from comm data or remote sensing data, so the methods
designed for comm or remote sensing applications will fail.

The OP has data that are sufficiently different from the
data one usually deals with in DSP, so he should seek
advice from people who deal with data with the same
characteristics as his.

Rune