[SciPy-User] frequency components of a signal buried in a noisy time domain signal
Nils Wagner
nwagner@iam.uni-stuttgart...
Sat Feb 27 03:41:03 CST 2010
On Fri, 26 Feb 2010 16:32:01 -0500
Anne Archibald <peridot.faceted@gmail.com> wrote:
> Hi,
>
> Looking at a periodic signal buried in noise is a
>well-studied
> problem, with many techniques for attacking it. You
>really need to be
> a little more specific about what you want to do. For
>example, is your
> input signal really a sinusoid, or does it have harmonic
>content? Are
> you trying to detect a weak periodic signal or are you
>trying to
> extract the features of a strong periodic signal? Is
>your signal
> exactly periodic, does it have some (deterministic or
>random) wander,
> or are you looking for the power spectrum of a broadband
>signal?
>
> If your input data are non-uniformly sampled, everything
>becomes more
> difficult (and computationally expensive), but there are
>solutions
> (e.g. the Lomb-Scargle periodogram).
>
> Anne
>
Hi Anne,
Thank you very much for your hints !
BTW, a BSD licensed code for the Lomb-Scargle periodogram
is available at
http://www.mathworks.com/matlabcentral/fileexchange/993-lombscargle-m
http://www.mathworks.com/matlabcentral/fileexchange/20004-lomb-lomb-scargle-periodogram
I am newbie to signal processing.
Is there a good introduction that you can recommend ?
There are so many books on signal processing. It should
cover engineering applications.
What makes a signal weak/strong periodic ?
The signals come from real-life application (pressure /
acceleration data).
Do I need a filter before I apply FFT ?
What would you do if you know nothing about the origin of
the signal ?
How can I distinguish between deterministic and random
wander ?
Cheers,
Nils
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