On 12 Okt, 13:12, Rune Allnor <all...@[EMAIL PROTECTED]
> wrote:
> On 12 Okt, 12:04, "Qian.S...@[EMAIL PROTECTED]
" <Qian.S...@[EMAIL PROTECTED]
> wrote:
>
> > a bandlimit white noise x(t) with PSD of S0 is sampled (no aliasing)
> > to produce x[n]. The PSD of x[n] is calculated to be S0/Ts (Ts is the
> > sample period).
> > Now I just reconstruct the continuous noise xr(t) by passing x[n]
> > impulses to the ideal reconstruction filter (gain=Ts, -fs<f<fs). The
> > output PSD is calculated to be S0/Ts*Ts^2=S0*Ts. There is an offset
> > from the input noise PSD by a ratio of Ts!
> > There must be some scaling error in above statement because ideal
> > sampling and reconstructing a bandlimit white noise should produce
> > itself. Please correct me!! Thanks!!
>
> First, if the missing scaling factor is 1/Ts I would check
> out the definitions of the Fourier transforms. With the discrete-
> domain DFT there is a 'skewness' between the forward and inverse
> transforms. The scale factor is 1 in the forward transform and
> 1/N in the inverse tranform.
Actually, I think the problem is to preserve the physical
dimensions through the sampling. DSP algorithms work on
dimensionless data while you seem to work with physical
data in the sense that dimensions and scales are preseved.
The missing 1/Ts [s] factor is consistent with that you
seem to expect an answer in dimension [Hz], which you
don't get.
So the first place to look for errors is to work through
the ADC model in painstaking detail and make sure all the
scaling factors etc are preserved. The result of this
exercise would be a constant scaling factor, so it would
have little effect on the overall algorithm. And don't be
surprised if the factor turns out to be 1/Ts...
This is one of thise things people tend to skip unless one
works with physics simulators or DSP in calibrated systems.
I have done neither, so I can't help out with the details.
Rune
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