Hi, Did anybody know how to implement cross correlation function? Thanks. It is not gr_simple_correlator, is it? Thanks
on 2006-03-24 17:31
on 2006-03-27 21:10
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!
on 2006-03-28 00:52
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/m... (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)