From eb89a48ab517e3813ed2941bacef5f0700f572c4 Mon Sep 17 00:00:00 2001 From: Yuval Adam <_@yuv.al> Date: Tue, 8 May 2018 16:45:33 +0200 Subject: More talk slides --- talk.slides.html | 223 +++++++++++++++++++++++++++++++++++++++++++++++++++---- 1 file changed, 209 insertions(+), 14 deletions(-) (limited to 'talk.slides.html') diff --git a/talk.slides.html b/talk.slides.html index ba87abf..216dade 100644 --- a/talk.slides.html +++ b/talk.slides.html @@ -11905,7 +11905,34 @@ a.anchor-link {
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Pulling Radio Data out of Thin Air

Pycon Israel 2018

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Pulling radio data out of thin air

Pycon Israel 2018

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Yuval Adam

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  • Full stack developer and systems architecture consultant
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  • But I also like to play with radios
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  • https://yuv.al
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  • @yuvadm
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This talk

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  • I never learned physics, RF engineering or signal processing
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  • But radios are pretty cool!
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  • Hopefully in 25 minutes I can show you some neat things
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Agenda

  • What are radio waves?
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  • Hardware radios
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  • Hardware vs software radio
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Radio Waves

static/dipole.gif

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Radio Waves

static/dipole.gif

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RTL-SDR

rtlsdr.jpg

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What's it good for

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  • Digital TV broadcast ("Idan+", DVB-T)
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  • Airplane tracking (ADS-B)
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  • Weather satellites
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  • FM radio broadcast
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I/Q Sampling

static/cosample.png

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Modulations

static/amfm2.gif

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import numpy as np
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modulator_frequency = 4.0
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plt.subplot(4, 1, 1)
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# pyrtlsdr provides us bindings to work with the RTL-SDR driver
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+from rtlsdr import RtlSdr
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+sdr = RtlSdr()
+sdr.sample_rate = 1.2e6       # 1,200,000 samples per second
+sdr.center_freq = 91.8e6      # 91,800,00 Hz frequency for the radio station
+sdr.gain = 'auto'             # tune the gain (AKA "volume") automatically
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+samples = sdr.read_samples(8192000)  # collect samples during 5 seconds
+sdr.close()
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+print(samples[:5])
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# Load the samples into a numpy array
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+import numpy as np
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+samples = np.array(samples).astype('complex64')
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# We captured too many samples so we need to apply a low pass filter
+# In other words, we have a too large window and we want to make it smaller
 
+import scipy.signal as signal
 
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+BANDWIDTH = 200e3
+DECIMATION_RATE = int(1.2e6 / BANDWIDTH)
 
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+samples = signal.decimate(samples, DECIMATION_RATE)
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# Apply the demodulation, we use a polar discriminator
 
+samples = np.angle(samples[1:] * np.conj(samples[:-1]))
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# De-emphasis filter - too "sciency"
+# MAKE THINGS SOUND BETTER
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+d = BANDWIDTH * 75e-6
+x = np.exp(-1/d)
+b, a = [1-x], [1,-x]
+samples = signal.lfilter(b, a, samples)
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# Decimate the signal down to something an audio driver can handle
+# We only catch the mono part of the signal
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+AUDIO_RATE = 50e3
+DECIMATION_RATE = int(1.2e6 / BANDWIDTH / AUDIO_RATE)
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+samples = signal.decimate(samples, DECIMATION_RATE)
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# HIT IT
 
+whatever.play(samples)
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Caveats

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  • Python, NumPy and SciPy are very good at doing fast processing of static data
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  • When handling real-time data, buffering becomes a serious issue
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  • GNU Radio
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    • top-notch framework
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    • implemented many signal processing primivites
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    • has a great scheduling engine
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