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)
TDOA_real2 = getRealTDOA(source_pos, micC, micD)
# Get Real Data
angleReal = 90 - angle_degree(
getAngle_angle1(getPoint(0, 0), source_pos))
distanceReal = distance(source_pos, getPoint(0, 0))
# 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()
for i in range(0, len(micList)):
for j in range(0, len(micList)):
if (i != j):
window=2000)
TDOA_real1 = getRealTDOA(source_pos, micA, micB)
TDOA_real2 = getRealTDOA(source_pos, micC, micD)
# SSL - TDOA_LIN
estimationLIN = SSL_TDOA_LIN(TDOA_CSOM1, TDOA_CSOM2, micA, micB,
micC, micD)
# SSL - TDOA_NOL
estimationNOL = SSL_TDOA_NOL(TDOA_CSOM1, TDOA_CSOM2, micA, micB,
micC, micD, estimationLIN)
# Distance
distanceReal = distance(source_pos, getPoint(0, 0))
angleReal = 90 - angle_degree(
getAngle_angle1(getPoint(0, 0), source_pos))
distanceLIN = distance(source_pos, estimationLIN)
distanceNOL = distance(source_pos, estimationNOL)
angleLIN = 90 - angle_degree(
getAngle_angle1(getPoint(0, 0), estimationLIN))
angleNOL = 90 - angle_degree(
getAngle_angle1(getPoint(0, 0), estimationNOL))
# print("angles ° ",angleReal,angleLIN,angleNOL)
# print("distances m ",distanceReal,distanceLIN, distanceNOL)
TDOA_error1 = np.sqrt(
(TDOA_real1 - TDOA_CSOM1) * (TDOA_real1 - TDOA_CSOM1))
TDOA_error2 = np.sqrt(
(TDOA_real2 - TDOA_CSOM2) * (TDOA_real2 - TDOA_CSOM2))
for j in range(0,7):
if(i!=j):
rI = estim_K/np.sqrt(getSignalPower_UsingTime_AverageFree(np.asarray(signals[i])))
rJ = estim_K/np.sqrt(getSignalPower_UsingTime_AverageFree(np.asarray(signals[j])))
solutions = getIntersectionPointsCircle(convertPoint(config["microphone_positions"][i]), rI, convertPoint(config["microphone_positions"][j]), rJ)
if(len(solutions)!=0):
estimPoint = plausibleFilter_AMP(solutions)
estimPoints.append(estimPoint)
# Ergebnisse auswerten
array_center = getPoint(0,0)
distList = list()
anglList = list()
for p in estimPoints:
distList.append(distance(array_center,convertPoint(p)))
anglList.append(getAngle_angle1(array_center, convertPoint(p)))
d = np.average(distList)
a = np.average(anglList)
estimPoint = getPoint(d*np.cos(a),d*np.sin(a))
# Plot Results
fig = plt.figure(figsize=(15,5))
plt.subplot(1,3,1)
plt.plot(K_list, label="sorted K list")
plt.plot(np.arange(len(K_list)-len(K_list_filt),len(K_list)),K_list_filt, label="filtered K list")
plt.hlines(estim_K, 0, 80, label="estimated K", colors="red")
plt.hlines(real_K, 0, 80, label="real K", colors="green")
plt.vlines(FILTER_LOW,0,1,label="filter_low")
plt.vlines(FILTER_HIGH,0,1,label="filter_high")
plt.legend(loc=4)
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=300)
TDOA_CSOM2, t, csom = tdoa_csom(signalCF, signalDF, fs=meta_data["sampling_rate"], window=300)
TDOA_real1 = getRealTDOA(source_pos, micA, micB)
TDOA_real2 = getRealTDOA(source_pos, micC, micD)
# Get Real Data
angleReal = 90-angle_degree(getAngle_angle1(getPoint(0,0), source_pos))
distanceReal = distance(source_pos, getPoint(0,0))
# Determine SSL LIN
estimationLIN = SSL_TDOA_LIN(TDOA_CSOM1, TDOA_CSOM2, micA, micB, micC, micD)
if(estimationLIN=="no"):
print(arr, ";", signal, ";", angle, ";", dist, ";", pNoise, ";", pSigna, ";", snr_DB, ";", TDOA_real1, ";", TDOA_real2, ";", TDOA_CSOM1, ";", TDOA_CSOM2, ";", angleReal, ";", "nan", ";", "nan", ";", distanceReal, ";", "nan", ";", "nan")
print(arr, ";", signal, ";", angle, ";", dist, ";", pNoise, ";", pSigna, ";", snr_DB, ";", TDOA_real1, ";", TDOA_real2, ";", TDOA_CSOM1, ";", TDOA_CSOM2, ";", angleReal, ";", "nan", ";", "nan", ";", distanceReal, ";", "nan", ";", "nan", file=f)
continue;
# Determine SSL NOL
estimationNOL = SSL_TDOA_NOL(TDOA_CSOM1, TDOA_CSOM2, micA, micB, micC, micD, estimationLIN)
# Fehler Auswertung
angleLIN = 90-angle_degree(getAngle_angle1(getPoint(0,0), estimationLIN))
angleNOL = 90-angle_degree(getAngle_angle1(getPoint(0,0), estimationNOL))
for j in range(0, len(micList)):
if(i!=j):
a = getRealTDOA(source_pos, micList[i], micList[j])
b = getFakeTDOA(a, 96000)
K1_exakt, K2_exakt = estimateK_Pair(signalsPower[i], signalsPower[j], micList[i], micList[j], a*343.2)
K1_noised, K2_noised = estimateK_Pair(signalsPower[i], signalsPower[j], micList[i], micList[j], b*343.2)
K_estim_exakt.append(K1_exakt)
K_estim_exakt.append(K2_exakt)
K_estim_noise.append(K1_noised)
K_estim_noise.append(K2_noised)
# Remove NAN
K_estim_exakt = [x for x in K_estim_exakt if str(x) != 'nan']
K_estim_noise = [x for x in K_estim_noise if str(x) != 'nan']
K_estim_exakt.sort()
K_estim_noise.sort()
K_estim = K_estim_noise[int(len(K_estim_noise)*xval)]
# Distance
distanceReal = distance(source_pos, getPoint(0,0))
angleReal = 90 - angle_degree(getAngle_angle1(getPoint(0,0), source_pos))
# Distance Estimated
radiusList = list()
for i in range(0,len(micList)):
radiusList.append(np.sqrt(K_estim/signalsPower[i]))
distanceEstim = np.average(radiusList)
print(dis, ";", ang, ";", K_true, ";", K_estim, ";", distanceReal, ";", distanceEstim)
print(dis, ";", ang, ";", K_true, ";", K_estim, ";", distanceReal, ";", distanceEstim, file=f)
f.close()
