Hi,

Did anybody know how to implement cross correlation function? Thanks.

It is not gr_simple_correlator, is it?

Thanks

# Cross Correlation Function

Sunflower:

the correlator may be used. You may also use the filtering routine by

making the appropriate changes to the waveform you wish to cross

correlate against the incoming signal such as reverse in time and

complex conjugate. What are your needs?

sunflower wrote:

Hi,

Did anybody know how to implement cross correlation function? Thanks.

It is not gr_simple_correlator, is it?

Thanks

–

AMSAT VP Engineering. Member: ARRL, AMSAT-DL, TAPR, Packrats,

NJQRP/AMQRP, QRP ARCI, QCWA, FRC. ARRL SDR Wrk Grp Chairman

Laziness is the number one inspiration for ingenuity. Guilty as

charged!

sunflower wrote:

If you want a generic correlation function then you can do correlation

in the frequency domain quite efficiently.

See the code below on how to do this:

This code is very well suited if you want the whole correlation function

over a reletively large time-frame.

This code is maybe not the best way when you only want to know where the

correlation peak is and already know where it approximately should be.

You can also find this code on:

http://www.olifantasia.com/pub/projects/gnuradio/mdvh/passive_radar

(Look for the file named correlator.py)

I extracted it from my passive radar experiments code which you also can

find there.

(But which are not very readable)

class correlator_c(gr.hier_block):

def **init**(self, fg, fft_size=512,output_type=‘COMPLEX’):

#This Hier_block expects an input block with two interleaved

gr_complex signals

#It outputs fft_size blocks with time zero at the middle of the

block

#Output type can be chosen between ‘COMPLEX’, ‘REAL’, ‘MAG’ or

‘ARG’

#

#You can use it in the following way:

# interleaver= gr.interleave(gr.sizeof_gr_complex)

# fg.connect(src0,(interleaver,0))

# fg.connect(src1,(interleaver,1))

#

corr=correlator.correlator_c(fg=fg,fft_size=512,output_type=‘COMPLEX’)

# fg.connect(interleaver,corr)

#

```
di = gr.deinterleave(gr.sizeof_gr_complex)
s2p_a = gr.serial_to_parallel(gr.sizeof_gr_complex, fft_size)
s2p_b = gr.serial_to_parallel(gr.sizeof_gr_complex, fft_size)
s2p3 = gr.serial_to_parallel(gr.sizeof_gr_complex, fft_size)
p2s_a = gr.parallel_to_serial(gr.sizeof_gr_complex, fft_size)
p2s_b = gr.parallel_to_serial(gr.sizeof_gr_complex, fft_size)
mywindow = fftsink.window.blackmanharris(fft_size)
fft_a = gr.fft_vcc(fft_size, True, mywindow)
fft_b = gr.fft_vcc(fft_size, True, mywindow)
ifft=gr.fft_vcc(fft_size, False, mywindow)
conj=gr.conjugate_cc()
mult=gr.multiply_cc()
#get the ffts of the input signals (go from time to frequency
```

domain)

fg.connect((di,0),s2p_a,fft_a,p2s_a)

fg.connect((di,1),s2p_b,fft_b,p2s_b)

```
#do the correlation in the frequency domain
fg.connect(p2s_a,conj)
fg.connect(p2s_b,(mult,0))
fg.connect(conj,(mult,1))
#transform back to the time domain
fg.connect(mult,s2p3,ifft)
if output_type=='REAL':
c2real = gr.complex_to_real(fft_size)
elif output_type=='MAG':
c2real=gr.complex_to_mag(fft_size)
elif output_type=='ARG':
c2real=gr.complex_to_arg(fft_size)
if output_type=='COMPLEX':
sink=ifft
else:
fg.connect(ifft,c2real)
sink=c2real
gr.hier_block.__init__(self, fg, di, sink)
```