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import numpy as np
import scipy.signal as signal
from rtlsdr import RtlSdr
class NumpyFmDemod():
'''
Numpy-based FM signal demodulation
Based on the great tutorial by Fraida Fund
https://witestlab.poly.edu/blog/capture-and-decode-fm-radio/
'''
def __init__(self, frequency, sample_rate=1140000, sample_count=8192000, dc_offset=250000):
self.freq = int(frequency)
self.sample_rate = sample_rate
self.sample_count = sample_count
self.dc_offset = dc_offset
def capture_samples(self):
sdr = RtlSdr()
sdr.sample_rate = self.sample_rate
sdr.center_freq = self.freq - self.dc_offset
sdr.gain = 'auto'
self.samples = sdr.read_samples(self.sample_count)
sdr.close()
def demod(self):
# Convert samples to a numpy array
x1 = np.array(self.samples).astype('complex64')
# Mix the samples back down to avoid DC offset
fc1 = np.exp(-1.0j * 2.0 * np.pi * self.dc_offset / self.sample_rate * np.arange(len(x1)))
x2 = x1 * fc1
# Downsample the signal to catch only the target frequency
BANDWIDTH = 200000 # wideband FM signal is always 200kHz
TAPS = 64
# Use Remez algorithm to design filter coefficients
lpf = signal.remez(TAPS, [0, BANDWIDTH, BANDWIDTH + (self.sample_rate / 2 - BANDWIDTH) / 4, self.sample_rate / 2], [1,0], Hz=self.sample_rate)
x3 = signal.lfilter(lpf, 1.0, x2)
decimation_rate = int(self.sample_rate / BANDWIDTH)
x4 = x3[0::decimation_rate]
decimated_rate = self.sample_rate / decimation_rate
y5 = x4[1:] * np.conj(x4[:-1])
x5 = np.angle(y5)
d = decimated_rate * 75e-6 # Calculate the # of samples to hit the -3dB point
x = np.exp(-1/d) # Calculate the decay between each sample
b = [1-x] # Create the filter coefficients
a = [1,-x]
x6 = signal.lfilter(b,a,x5)
audio_freq = 44100.0
dec_audio = int(decimated_rate / audio_freq)
Fs_audio = decimated_rate / dec_audio
x7 = signal.decimate(x6, dec_audio)
x7 *= 10000 / np.max(np.abs(x7))
x7.astype('int16').tofile('wbfm-mono.raw')
def run(self):
self.capture_samples()
self.demod()
if __name__ == '__main__':
nfd = NumpyFmDemod(frequency=96.3e6)
nfd.run()
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