"kyle" <kyleblay@blerk.org> wrote in
news:7emdnSY4qYbHv8HeRVn-uQ@giganews.com: 

>>You do understand you are doing a portion of a zoom FFT when you add
>>the complex values together.  You run the risk that your signal will
>>not be 
> in the 
>>exact center of the bin so it may not be seen by the FFT.
> 
> No I have no idea what you mean... a zoom FFT?  I'll search around to
> find out what you mean but if you have a nice succinct description of
> the effect you are talking about that would be super :)
> 
> 
>           
> This message was sent using the Comp.DSP web interface on
> www.DSPRelated.com
> 

I zoom FFT is where multiply by a rotating vector so that you can look at 
a subset of the spectrum. I know that what I just wrote is confusing.

Here is an example:

Let say that you are sampling at 51.2 KHz and that you are performing a 
1024 point FFT. You might create a power spectrum by squaring the real 
and imaginary components of the FFT. The frequency range would be 0 - 
25.6K divided into 512 bins of bandwidth 50 Hz. 

Now lets say you were really interested in just a small portion of the 0 
- 25.6K spectrum. A zoom FFT multiplies the input samples by e^jwt to 
translate a portion of the signal to DC and then a new FFT is calculated. 
A 10x zoom might be used create a new spectrum from 1000 - 3560 Hz with a 
resolution of 5 Hz in each bin. You don't know what happens outside of 
this region but you can see more detail in the band of interest. It also 
take 10 times as many samples since BT >= 1. 

-- 
Al Clark
Danville Signal Processing, Inc.
--------------------------------------------------------------------
Purveyors of Fine DSP Hardware and other Cool Stuff
Available at http://www.danvillesignal.com

>You do understand you are doing a portion of a zoom FFT when you add the 
>complex values together.  You run the risk that your signal will not be
in the 
>exact center of the bin so it may not be seen by the FFT.

No I have no idea what you mean... a zoom FFT?  I'll search around to find
out what you mean but if you have a nice succinct description of the effect
you are talking about that would be super :)


		
This message was sent using the Comp.DSP web interface on
www.DSPRelated.com

You do understand you are doing a portion of a zoom FFT when you add the 
complex values together.  You run the risk that your signal will not be in the 
exact center of the bin so it may not be seen by the FFT.

In article <XoednZD-d4pwG8reRVn-uQ@giganews.com>, "kyle" <kyleblay@blerk.org> 
wrote:
>Hi.
>
>I am implementing some FFT software and I'm pondering different ways of
>averaging FFTs..
>
>Normally most people average the amplitude of each FFT to reduce the
>variance of the noise.  
>
>It so happens that in my application we are sampling synchronously with
>the signal we are interested in (and it's centre-cell) so the phase of the
>desired signal between each FFT should be the same - this led me to
>conclude that perhaps more can be gained from averaging the original
>complex-number FFTs.  Presumably the noise suffers from random phase and
>this will help reduce the noise further, whereas the signal we're
>interested in has constant (and hence reinforcing) phase.  Performing the
>averaging in this fashoin does indeed show the noise floor falling.
>
>Is this be a genuine way to reduce the noise floor further?  
>
>Even if the signals were not synchronous you might imagine advancing the
>phase of each FFT bin to ensure the phases line up - so is the reason this
>method is not always used due to the cost of this phase shifting?
>
>Cheers!
>
>
>                
>This message was sent using the Comp.DSP web interface on
>www.DSPRelated.com

If sequential FFT complex value averages do not depend on previous data
segments (used for last FFT average), it would be a lot more efficient
to average the data segments and do a single FFT than to FFT each data
segment and average the FFT complex values. Windowing, if desired could
be done on the average of the data segments, once per FFT.

If the data segments are used in more then 1 FFT average (some data
overlap),  you may still be able to come up with something in a similar
manner that is more efficient .

Dirk

In rotating machine analysis this technology has been used for more
than 30 years and it is widely accepted. It is just called
time-synchronize averaging. You can search the Internet with these key
words and hundreds of companies will pop up.

kyle wrote:
> Hi.
>
> I am implementing some FFT software and I'm pondering different ways of
> averaging FFTs..
>
> Normally most people average the amplitude of each FFT to reduce the
> variance of the noise.
>
> It so happens that in my application we are sampling synchronously with
> the signal we are interested in (and it's centre-cell) so the phase of the
> desired signal between each FFT should be the same - this led me to
> conclude that perhaps more can be gained from averaging the original
> complex-number FFTs.  Presumably the noise suffers from random phase and
> this will help reduce the noise further, whereas the signal we're
> interested in has constant (and hence reinforcing) phase.  Performing the
> averaging in this fashoin does indeed show the noise floor falling.
>
> Is this be a genuine way to reduce the noise floor further?

This is called coherent averaging. It is legitimate. Radars do this all
the time.

>
> Even if the signals were not synchronous you might imagine advancing the
> phase of each FFT bin to ensure the phases line up - so is the reason this
> method is not always used due to the cost of this phase shifting?
>
> Cheers!
>

In other words if you synchronize the signals then you can use coherent
averaging? Certainly true, but if synchronization is lost then results
can be disastrous -- you could get perfect cancellation!

John

Hi.

I am implementing some FFT software and I'm pondering different ways of
averaging FFTs..

Normally most people average the amplitude of each FFT to reduce the
variance of the noise.  

It so happens that in my application we are sampling synchronously with
the signal we are interested in (and it's centre-cell) so the phase of the
desired signal between each FFT should be the same - this led me to
conclude that perhaps more can be gained from averaging the original
complex-number FFTs.  Presumably the noise suffers from random phase and
this will help reduce the noise further, whereas the signal we're
interested in has constant (and hence reinforcing) phase.  Performing the
averaging in this fashoin does indeed show the noise floor falling.

Is this be a genuine way to reduce the noise floor further?  

Even if the signals were not synchronous you might imagine advancing the
phase of each FFT bin to ensure the phases line up - so is the reason this
method is not always used due to the cost of this phase shifting?

Cheers!


		
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