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()