def envelope_detector(signal, frecuency, sampling_rate):
""" Returns the envelope of the signal received in the transducer.
The signal is multiplied by the carrier signal (in phase), and then low pass
filtered."""
angular_frecuency = 2 * np.pi * frecuency
nyquist_frequency = sampling_rate / 2
frecuency_width = 100
filter_size = 11
phase = pll.pll(signal, angular_frecuency, sampling_rate)
time = np.arange(0, signal.size) / (1.0 * sampling_rate)
demodulated_signal = np.multiply(signal, np.sin(angular_frecuency * time + phase))
lowpass = scipysignal.firwin(filter_size, cutoff=frecuency_width, nyq=nyquist_frequency)
demodulated_signal = scipysignal.lfilter(lowpass, 1, demodulated_signal)
# The energy of the signal splits into a DC and twice the frecuency
# components, so it must be multiplied by two.
return 2 * np.abs(demodulated_signal)
def estimate_tof(self, signals):
# Average all the signals and calculate the envelope
average = uniform_sampled_signal.average(signals)
envelope = utilities.get_signal_envelope(average)
# Find the intesection of the signals with a certain level.
# To improve accuracy take tree slightly diffent levels and average.
thresholds = []
for thresholds_level in self.thresholds_levels:
thresholds.append(self.get_intersection(envelope, thresholds_level))
coarse_tof = (thresholds[0]+thresholds[1]+thresholds[2])/3
# Using the information of the main frecuency in the response we now fit
# the coase stimation to fit the phase of this wave
f = 27100 # carrier frecuency in the response
w = 2*np.pi*f # associated angular frecuecy
T = 1.0/f # associated period
phase = pll.pll(average, w)
# get the number of the cycle that contains the coarse estimation
n_cycle = np.floor((coarse_tof+(phase+np.pi)/w)/T)
fine_tof = -phase/w+n_cycle*T
print "%e"%fine_tof
return fine_tof, phase
def envelope_detector(signal, frecuency, sampling_rate):
""" Returns the envelope of the signal received in the transducer.
The signal is multiplied by the carrier signal (in phase), and then low pass
filtered."""
angular_frecuency = 2*np.pi*frecuency
nyquist_frequency = sampling_rate/2
frecuency_width = 100
filter_size = 11
phase = pll.pll(signal, angular_frecuency, sampling_rate)
time = np.arange(0, signal.size)/(1.0*sampling_rate)
demodulated_signal = np.multiply(signal,
np.sin(angular_frecuency*time+phase))
lowpass = scipysignal.firwin(filter_size, cutoff = frecuency_width,
nyq = nyquist_frequency)
demodulated_signal = scipysignal.lfilter(lowpass, 1, demodulated_signal)
# The energy of the signal splits into a DC and twice the frecuency
# components, so it must be multiplied by two.
return 2*np.abs(demodulated_signal)
