My advice would be to go read up on downsampling (especially anti-aliasing filters). :)
The short answer is you need to low pass filter the incoming signal so when you perform the down sampling you do not get aliasing of the signal. Kieran On Wed, Apr 1, 2009 at 10:47 AM, E. Ornelas <a33...@ua.pt> wrote: > > Hello again! > > Thanks for the advice. But the problem is that during the file capture I > was > asked to have a resolution of about 1hz or 2hz on a bandwidth of about > 200hz. I would like to have at that point enough decimation to sample at > about 500hz. So I think I'll need the decimation working correctly in the > final program. > Won't this effect I'm seeing apear too? > > Right now I can have an accurate estimation until the hundreds. I was > hoping > to get the tens and units correct as well or with an error of 1hz or 2hz. > It's very important to have this resolution given the fact that the power > is > mostly in a 100hz width. With decimation I have an error of about 60hz and > it was supposed to be around 6hz to 12hz. > > Sorry if I'm repeating my self over and over. But it's very important to > solve this problem to advance in my thesis project. > > Thanks in advance. > > Emanuel Ornelas > > > > E. Ornelas wrote: > > > > Hello! > > > > I'm doing a project where i need to tune a signal around 10.7Mhz, and > then > > start making a capture to a file. > > So I need to the a tuner which searches for the correct center frequency, > > so that I can narrow the sampling band as much as possible. > > > > The idea I'm trying to implement is a two step search. > > On the first I use a 250khz band using only USRP decimation, and a 4096 > > points fft. This way the center frequency has an accuracy of about 120hz. > > On the second step I narrow the band to 25kHz, decimating 10 times the > > signal > > from the usrp using software decimation. It's implemented using > > gr.keep_one_in_n(type,10). Keeping the same FFT size there should be an > > error of about 12hz. > > > > Now the problem is this: the estimation from the first step is closer to > > the real value, read on a spectrum analyzer, than the one in the second. > > Since I have the same number of points for the FFT, shouldn't the > > resolution per bin be 10 times better because the bandwidth is 10 times > > narrower? > > Do I need to change the way I'm decimating the signal? (Not using > > keep_one_in_n ...) > > Why am I getting poorer results? > > > > This is the 2-step tuner I have for now: > > > > #////////////////////////////////////////////////////////// > > # Set initial values so start signal search with the tunner > > #////////////////////////////////////////////////////////// > > > > cf = 10.701200e6 # center frequency > > fs = 250.0e3 # sampling frequency > > fsize = 4096 # number of point on the FFT > > > > print > > print "Initilizing Tunner.." > > tb = tunner(cf, fs, fsize) > > print "1st Search.." > > tb.start() > > > > # Waiting period, to avoid initial flunctuations > > time.sleep(5) > > > > # Get 500 samples to make the first estimation > > while(len(tb.v_sink.out_freq()) < 30): > > pass > > tb.stop() > > tb.wait() > > print "Search done!" > > > > # Make evaluation > > # @ get magnitude vector > > # @ get frequency vector > > print "Evaluating.." > > > > mg = scipy.array(tb.v_sink2.out_mag()) > > fq = scipy.array(tb.v_sink.out_freq()) > > > > print "MAG:", mg > > print "FREQ:", fq > > > > if mg.mean() > -10: > > cf = fq.mean() > > print "Center Frequency = ", cf > > print > > elif mg.mean() <= -10: > > print "Not enough power!!" > > print > > > > > > #////////////////////////////////////////////////////// > > # Set values to fine tunne the frequency before capture > > #////////////////////////////////////////////////////// > > tb = None > > > > cf = 10.701200e6 > > fs = 25.0e3 > > fsize = 4096 > > > > print > > print "Initilizing Tunner.." > > tb = tunner(cf, fs, fsize) > > print "2nd Search.." > > tb.start() > > > > # Waiting period, to avoid initial flunctuations > > time.sleep(5) > > > > # Get 30 samples to make the first estimation > > while(len(tb.v_sink.out_freq()) < 30): > > pass > > tb.stop() > > tb.wait() > > print "Search done!" > > > > # Make evaluation > > # @ get magnitude vector > > # @ get frequency vector > > print "Evaluating.." > > > > mg = scipy.array(tb.v_sink2.out_mag()) > > fq = scipy.array(tb.v_sink.out_freq()) > > > > print "MAG:", mg > > print "FREQ:", fq > > > > if mg.mean() > -10: > > cf = fq.mean() > > print "Center Frequency = ", cf > > print > > elif mg.mean() <= -10: > > print "Not enough power!!" > > > > > > And this is the part of the __init__ that concerns the decimation: > > (buffer_sink is a block made by me that implement a circular buffer to > > store values) > > > > class tunner(gr.top_block): > > def __init__(self, center_frequency, sampling_frequency, fft_size): > > gr.top_block.__init__(self) > > > > (....) > > > > full_decim = int(64e6/sampling_frequency) > > > > print "FULL DECIM = ", full_decim > > > > if full_decim > 256: > > decim = 256 > > sw_decim = gr.keep_one_in_n(gr.sizeof_gr_complex, > > int(250e3/sampling_frequency)) > > print "SW = ", int(250e3/sampling_frequency) > > elif full_decim <= 256: > > decim = full_decim > > sw_decim = gr.keep_one_in_n(gr.sizeof_gr_complex, > 1) > > print "SW = 0" > > > > (....) > > > > self.connect(self.u, sw_decim, ss2v, fft, c2m, (imax,0), > > s2f1, self.buffer_sink) > > self.connect((imax,1), s2f2, p1) > > > > -- > View this message in context: > http://www.nabble.com/Software-Decimation---problem-estimating-frequency-tp22804977p22815529.html > Sent from the GnuRadio mailing list archive at Nabble.com. > > > > _______________________________________________ > Discuss-gnuradio mailing list > Discuss-gnuradio@gnu.org > http://lists.gnu.org/mailman/listinfo/discuss-gnuradio >
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