def generator(Fs, i):
print("Signal iteration", i)
T = np.float(6e-3) # Pulse duration
# Time domain window for NLFM generation
NLFM = radar.chirp(Fs)
NLFM.fftLen = 2048
sigObj = waveform()
sigObj.Fs = Fs
sigObj.T = T
sigObj.symbolRate = 1 / T
# Synthesize the target autocorrelation function
#window_t = signal.chebwin(np.intc(2048), 60)
window_t = signal.hamming(np.intc(2048))
#window_t = signal.gaussian(np.intc(2048), 360)
#window_t = signal.gaussian(np.intc(2048), 400)
"""
# Random BW
fStart = random.uniform(10e3,100e3)
fStop = fStart+random.uniform(10e3,100e3)
fCenter = fStop-(fStop-fStart)/2
path = '../../waveforms/'
"""
# Fixed BW
fStart = random.uniform(10e3, 100e3)
fStop = fStart + 50e3
fCenter = fStop - (fStop - fStart) / 2
sigObj.fCenter = fCenter
sigObj.fStart = fStart
sigObj.fStop = fStop
path = '../waveforms/'
sigObj.polynomial = NLFM.getCoefficients(window_t,
targetBw=fStop - fStart,
centerFreq=fCenter,
T=T)
sigObj.omega_t = NLFM.targetOmega_t
# Center frequency is defined as the estimated center frequency in infinite SNR
sig_t = NLFM.genNumerical()
#sigObj.fCenter = estimate.carierFrequencyEstimator(sig_t, Fs, method='mle', nfft=len(sig_t))
# Write to binary file
filename = str(i)
destination = path + filename + '.pkl'
# Save sigObj to binary file
with open(destination, 'wb') as f:
pickle.dump(sigObj, f)
imagePath = '../figures/ModChirpPsd/' import numpy as np import rftool.radar as radar import rftool.utility as util Fs = np.intc(802e3) # receiver sample rate T = np.float(6e-3) # Pulse duration points = np.intc(Fs * T) t = np.linspace(0, T, points) targetBw = 50e3 # Pulse BW centerFreq = 75e3 # Pulse center frequency # Generate chirp NLFM = radar.chirp(Fs) window_t = signal.hamming(np.intc(2048)) NLFM.getCoefficients(window_t, targetBw=targetBw, centerFreq=centerFreq, T=T) NLFMsig = NLFM.genNumerical() #! Study a single symbol """sig_t = NLFM.modulate( np.array([1]) ) util.periodogram(sig_t, Fs)""" # Periodogram of packet bitstream = np.random.randint(0, 2, 32) sig_t = NLFM.modulate(bitstream) #util.periodogram(sig_t, Fs) # Calculates Power Spectral Density in dBW/Hz. fftLen = np.intc(2**15)
fCenterEstimate = np.zeros((nIterations, len(snrVector)), dtype=np.float64)
fCenterEstimate2 = np.zeros((nIterations, len(snrVector)), dtype=np.float64)
R_symbEstimate = np.zeros((nIterations, len(snrVector)), dtype=np.float64)
R_symbEstimate2 = np.zeros((nIterations, len(snrVector)), dtype=np.float64)
for i in range(0, nIterations):
print("Iteration", i+1, "of", nIterations)
# Load from binary file
filename = str(i)
fileString = path + filename + ".pkl"
with open(fileString,'rb') as f:
sigObj = pickle.load(f)
NLFM = radar.chirp(sigObj.Fs)
NLFM.targetOmega_t = sigObj.omega_t
NLFM.points = len(sigObj.omega_t)
NLFM.c = sigObj.polynomial
packetSize=32
package = np.random.randint(0, 2, packetSize)
modSig = NLFM.modulate( package )
targetR_symb = 1/NLFM.T
# Calculate CRLB for the chirp in the SNR range
if(i == 0):
# Generate pulse
#p_n = NLFM.genFromPoly()
p_n = NLFM.genNumerical()
# Number of pulses
def estimate(i, SNRVector, estimator, packetSize=1, **kwargs):
print(estimator.name, 'iteration', i)
time = np.empty_like(SNRVector)
AE = np.empty_like(SNRVector)
if signalType == 'NLFM':
# Load from binary file
filename = str(i)
fileString = self.path + filename + ".pkl"
with open(fileString, 'rb') as f:
m_waveform = pickle.load(f)
FM = radar.chirp(m_waveform.Fs)
FM.targetOmega_t = m_waveform.omega_t
FM.points = len(m_waveform.omega_t)
if 1 < packetSize:
bitstream = np.random.randint(0, 2, packetSize)
m_waveform.packet = bitstream
sig_t = FM.modulate(bitstream)
else:
sig_t = FM.genNumerical()
m_waveform.packet = 1
elif signalType == 'LFM':
m_waveform = kwargs.get('m_waveform')
# Fixed BW, Random Center Frequency
fStart = random.uniform(10e3, 100e3)
fStop = fStart + 50e3
fCenter = fStop - (fStop - fStart) / 2
m_waveform.fCenter = fCenter
m_waveform.fStart = fStart
m_waveform.fStop = fStop
FM = LFM.chirp(
Fs=m_waveform.Fs,
T=m_waveform.T,
fStart=fStart,
fStop=fStop,
nChirps=8,
direction='both'
) # Both directions and 8 symbols are configured in order to generate the sync sequence.
if 1 < packetSize:
addSyncSeq = kwargs.get('syncSeq', False)
if addSyncSeq == True:
synqSeqSymbols = np.array(
[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 4, 4, 4])
symbolStream = np.random.randint(
0, 4, packetSize - len(synqSeqSymbols))
synqSeq = FM.modulate(synqSeqSymbols)
# Remove the last three quartes of a symbol
clipLen = np.intc(FM.Fs * FM.T * 0.75)
synqSeq = synqSeq[:-clipLen]
# Add a random packet to the sync sequence
sig_t = FM.modulate(symbolStream)
sig_t = np.append(synqSeq, sig_t)
else:
symbolStream = np.random.randint(0, 4, packetSize)
m_waveform.packet = symbolStream
sig_t = FM.modulate(symbolStream)
else:
sig_t = FM.getSymbolSig(1)
m_waveform.packet = 1
for m, SNR in enumerate(SNRVector):
sig_noisy_t = util.wgnSnr(sig_t, SNR)
tic = timeit.default_timer()
# cleanSig and SNR is passed to enable CRLB calculation, fCenter for a-priori freq info for some estimators
estimate = estimator.function(sig_noisy_t,
**estimator.kwargs,
SNR=SNR,
cleanSig=sig_t,
fCenterPriori=m_waveform.fCenter)
toc = timeit.default_timer()
# Calculate Accumulated Absolute Error
# For packets, the inverse of the bitstream is accepted.
if parameter == 'packet':
AE_temp = []
AE_temp.append(
np.sum(
np.abs(
np.subtract(m_waveform.returnValue(parameter),
estimate))))
AE_temp.append(
np.sum(
np.abs(
np.subtract(m_waveform.returnValue(parameter),
abs(1 - estimate)))))
AE[m] = np.min(AE_temp)
# If the error is greater than 50% then (for binary symbols) the packet is broken.
if len(estimate) / 2 < AE[m]:
AE[m] = len(estimate) / 2
elif 'CRLB' in estimator.name:
AE[m] = estimate
else:
AE[m] = np.sum(
np.abs(
np.subtract(m_waveform.returnValue(parameter),
estimate)))
# calculate execution time
time[m] = toc - tic
return AE, time