[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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