Re: signal detection in doppler

On May 25, 2:34 pm, "cpshah99" <cpsha...@xxxxxxxxxxxxxx> wrote:
On May 25, 4:32 am, "cpshah99" <cpsha...@xxxxxxxxxxxxxx> wrote:
On May 23, 10:54 am, "cpshah99" <cpsha...@xxxxxxxxxxxxxx> wrote:
On May 19, 9:28 am, "cpshah99" <cpsha...@xxxxxxxxxxxxxx> wrote:
Hello People

I have been scratching my head on this doppler problem:

Because of the doppler effect, the received signal will
either
be
expanded
or compressed on time axis.

Now, the expansion or compression is sampling rate
conversion.
So,
I
tried
to use 'resample' function of matlab. The actual length of
the
signal
is
30720 and sampling freq is 48000Hz.

Now, when I use y=resample(x,10010,10000) i can find the peak
i.e.
start
of my data by correlating the signal with training sequence.

But when I use y=resample(x,10007,10000) i can't find the
peak.

So is there any better way to
1. model this exapnsion or compression
or 2. to find peak when the received is corrupted by severe
doppler.

Help in this regard will be highly appreciated.

Chintan

Just clarify - a doppler shift isn't modeled by a frequency
shift.
It
is really a time dilation. For pure sinusoids it then appears as
a
frequency shift. If you signal doesn't have a lot of bandwidth
wrt
the
center frequency then you can approximate the doppler shift by
a
frequency shift.

Cheers,
David

%%%%%

Hi David

My center freq is 12KHz and BW is 4 KHz.

I understand as u said, the doppler effect is time dilation, but
should
I
just shift the center freq or sampling freq to approximate the
doppler
shift because if I shift carrier freq then it will just add
phase
shift
but
it will not expand or compress the signal.

Also I am reading Multirate Digital Signal Processing by
Crochiere
and
Rabiner to implement Farrow Interpolator, suggested by Julius.

Thanks you again.

Chintan.

What type of system is this? Sonar? What is the speed of
propagation
in your medium? What is the format of your signal? Is it a LFM or
HFM
pulse?

For these types of signals in sonar they use banks of matched
filters,
which are constructed by time dilating the analytic formula for
the
pulse. For more detail you can see Neilson's "Sonar Signal
Processing".

For Farrow information I found Harris's book quite good. I also
posted
some matlab code on this group to do derive the Farrow spline
coefficients - it doesn't do the online resampling.

Cheers,
David

%%%%%

Hi David

This system is for underwater communications, where speed of
acousticwave
is 1500 m/s. And also I am not using LFM or HFM. My signal format
is
500
symbols long PN sequence to find the start of that (not to do
anything
with
DSSS) and after this PN 2000 data symbols.

Can u please post that code here again, if you don't mind.

Thanks,

Chintan

It's on my other computer. I'll try to post it on Monday.

Given you're dealing with probably +- 30 knots doppler (or more) and
the band & bandwidth you should take into account the dilation,
but normally the PN , LFM , HFM signals have fairly broad ambiguity
functions so they still correlate over broad dopplers - you shouldn't
need that many doppler replicas to find a peak. Normally the replicas
are set at the -3dB points in the ambiguity function.

Cheers,
David

%%%%%%

Hi David,

Thanks for your replies.

Actually I have worked on the real received signals thru underwater
sea
trial experiment, the relative velocity estimation was in the range of
+-0.5 m/s, and in that case using PN I was able to find peak.

But now as I have changed my receiver architecture, and we can not go
for
sea trial as of now, so my advisor asked me to simulate doppler, which
is
signal expansion or compression.

I have been trying to this for last so many days.

I have used 'resample' function of matlab, but it changes the amplitude
of
the signal significantly. And in real time there is no zero padding
for
interpolation or decimation!!!!!!!

Thanking you again.

Chintan


Here's code I wrote to calculate the splines. You feed it the filter,
you'll need to adjust the Decimation Factor (D) to match your own
requirements and the filter.

Good luck.

Cheers,
Dave

% b = coefficients of a predesigned filter
% In my test case b has 300 taps
% Designed using 72 dB attenuation Kaiser window filter
bt = b;
%bt(1) = bt(2) -(bt(3)-bt(2));
%bt(end) = bt(1);

Ntaps=length(b);
D=50;
Npoly=Ntaps/D;
b2= reshape(bt,D,Npoly);
%For a good polynomial fit we need to handle the end points in a
smooth
%fashion
I=length(bt);
tmp = bt(I)- (bt(I-1)-bt(I));
%b2=[b2;b2(1,2:end),0];
b2=[b2;b2(1,2:end),tmp];
x=(0:D)'/D; %Dividing by D helps eliminate poor conditioning
p_ord = 6; %Order of polynomials for fitting
p=zeros(Npoly,p_ord+1);
for k =1:Npoly
k
p(k,:) =polyfit(x,b2(:,k),p_ord);
%This plots the error in the polynomial evaluation
%plot(b2(:,k)-polyval(p(k,:),x))
%pause
end

%Lets evaluate the resulting filter at some other sample point.

x2 = x(1:end-1)+0.5/D;
h2=[];
for k =1:Npoly
h2=[h2,polyval(p(k,:),x2)];
end
plot(20*log10(abs(fft(h2(:),2048))))

%%%%%%

Hi David,

Thank you very much. It is really appreciated.

Regards,

Chintan
.

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