noise_microphone = 0.003
noise_environment = 0.04
noise_source = 0.01
source_pos = getPoint(dis * np.sin(angle_radians(ang)),
dis * np.cos(angle_radians(ang)))
config = updateConfig(config, micA, micB, noise_microphone,
noise_environment, noise_source, source_pos)
# Starte Simulation
loaded = simulate(config, config["source_position"],
signal_function)
signals = loaded.get_measurements()
meta = loaded.get_meta_data()
signalA = signals[0]
signalB = signals[1]
signalAF = butterWorthFilter(signalA, meta["sampling_rate"], 1500)
signalBF = butterWorthFilter(signalB, meta["sampling_rate"], 1500)
TDOA_CSOM, t, csom = tdoa_csom(signalAF,
signalBF,
fs=meta["sampling_rate"],
window=1000)
TDOA_real = getRealTDOA(source_pos, micA, micB)
a, b, c, SNR = getSNR(signalAF)
SNRls.append(SNR)
TDOA_realls.append(TDOA_real)
TDOA_csomls.append(TDOA_CSOM)
print(dis, ";", ang, ";", source_pos, ";", np.average(SNRls), ";",
np.std(SNRls), ";", np.average(TDOA_realls), ";",
signalsPower = pSigna
# Determine TDOA
idxA = 8
idxB = 2
idxC = 2
idxD = 4
micA = micList[idxA - 1]
micB = micList[idxB - 1]
micC = micList[idxC - 1]
micD = micList[idxD - 1]
signalA = data[idxA]
signalB = data[idxB]
signalC = data[idxC]
signalD = data[idxD]
signalAF = butterWorthFilter(signalA, meta_data["sampling_rate"],
2000)
signalBF = butterWorthFilter(signalB, meta_data["sampling_rate"],
2000)
signalCF = butterWorthFilter(signalC, meta_data["sampling_rate"],
2000)
signalDF = butterWorthFilter(signalD, meta_data["sampling_rate"],
2000)
TDOA_CSOM1, t, csom = tdoa_csom(signalAF,
signalBF,
fs=meta_data["sampling_rate"],
window=200)
TDOA_CSOM2, t, csom = tdoa_csom(signalCF,
signalDF,
fs=meta_data["sampling_rate"],
window=200)
TDOA_real1 = getRealTDOA(source_pos, micA, micB)
noise_source = 0.01
source_pos = getPoint(dis * np.sin(angle_radians(ang)),
dis * np.cos(angle_radians(ang)))
config = updateConfig(config, micList, noise_microphone,
noise_environment, noise_source, source_pos)
# Signal Simulation
loaded = simulate(config, config["source_position"],
signal_function)
signals = loaded.get_measurements()
meta = loaded.get_meta_data()
signalsFiltered = list()
signalsPower = list()
signalsSNR = list()
for s in signals:
sf = butterWorthFilter(s, meta["sampling_rate"], 2000)
powerSig, powerNoi, snrFac, snrDB = getSNR(sf)
signalsFiltered.append(sf)
signalsPower.append(powerSig)
signalsSNR.append(snrDB)
# Calculate True K
K_true = 0
for k in range(0, len(micList)):
K_true += signalsPower[k] * distance(
micList[k], source_pos) * distance(micList[k], source_pos)
K_true /= len(micList)
# Calculate K estimation
K_estim_exakt = list()
K_estim_noise = list()
