def estimate_DOA(self):
#print("[ INFO ] Python DSP: Estimating DOA")
iq_samples = self.module_receiver.iq_samples[:, 0:self.DOA_sample_size]
# Calculating spatial correlation matrix
R = de.corr_matrix_estimate(iq_samples.T, imp="fast")
if self.en_DOA_FB_avg:
R = de.forward_backward_avg(R)
M = np.size(iq_samples, 0)
if self.DOA_ant_alignment == "UCA":
self.DOA_theta = np.linspace(0, 360, 361)
#scanning_vectors = de.gen_uca_scanning_vectors(M, self.DOA_inter_elem_space, self.DOA_theta)
x = self.DOA_inter_elem_space * np.cos(
2 * np.pi / M * np.arange(M))
y = self.DOA_inter_elem_space * np.sin(
-2 * np.pi / M * np.arange(M)) # For this specific array only
scanning_vectors = de.gen_scanning_vectors(M, x, y, self.DOA_theta)
# DOA estimation
if self.en_DOA_Bartlett:
self.DOA_Bartlett_res = de.DOA_Bartlett(R, scanning_vectors)
if self.en_DOA_Capon:
self.DOA_Capon_res = de.DOA_Capon(R, scanning_vectors)
if self.en_DOA_MEM:
self.DOA_MEM_res = de.DOA_MEM(R,
scanning_vectors,
column_select=0)
if self.en_DOA_MUSIC:
self.DOA_MUSIC_res = de.DOA_MUSIC(R,
scanning_vectors,
signal_dimension=1)
elif self.DOA_ant_alignment == "ULA":
self.DOA_theta = np.linspace(-90, 90, 181)
x = np.zeros(M)
y = np.arange(M) * self.DOA_inter_elem_space
scanning_vectors = de.gen_scanning_vectors(M, x, y, self.DOA_theta)
# DOA estimation
if self.en_DOA_Bartlett:
self.DOA_Bartlett_res = de.DOA_Bartlett(R, scanning_vectors)
if self.en_DOA_Capon:
self.DOA_Capon_res = de.DOA_Capon(R, scanning_vectors)
if self.en_DOA_MEM:
self.DOA_MEM_res = de.DOA_MEM(R,
scanning_vectors,
column_select=0)
if self.en_DOA_MUSIC:
self.DOA_MUSIC_res = de.DOA_MUSIC(R,
scanning_vectors,
signal_dimension=1)
print(self.DOA_MUSIC_res)
def DOA_demo(self):
self.logger.debug("-> Running simulation <-")
soi_theta = self.horizontalSlider_source_DOA.value()
M = self.spinBox_noa.value() # Number of antenna elements
N = 2**self.spinBox_sample_size.value()
r = self.doubleSpinBox_UCA_r.value()
d = self.doubleSpinBox_ULA_d.value()
K = 1 + self.spinBox_multipath_components.value()
alphas = [1.0]
thetas = [soi_theta]
phases = [0]
try:
multipath_alphas_str= self.lineEdit_multipath_amplitudes.text().split(',')
if self.checkBox_multipath_random_angles.isChecked():
multipath_angles_str = ""
for k in range(K-1):
theta_rnd=np.random.uniform(0,360)
multipath_angles_str += "{:3.1f},".format(theta_rnd)
if self.checkBox_multipath_random_phases.isChecked():
multipath_phases_str = ""
for k in range(K-1):
phase_rnd=np.random.uniform(0,360)
multipath_phases_str += "{:3.1f},".format(phase_rnd)
self.lineEdit_multipath_phases.setText(multipath_phases_str[:len(multipath_phases_str)-1])
multipath_angles_str= self.lineEdit_multipath_angles.text().split(',')
multipath_phases_str= self.lineEdit_multipath_phases.text().split(',')
# Add multipath parameters
for k in range(K-1):
alphas.append(float(multipath_alphas_str[k]))
thetas.append(float(multipath_angles_str[k]))
phases.append(float(multipath_phases_str[k]))
logging.debug("k: {:d}, alpha:{:f} phi:{:f} theta:{:f}".format(k,alphas[k+1],phases[k+1], thetas[k+1]))
self.label_status.setText("<span style=\" font-size:8pt; font-weight:600; color:#01df01;\" >Running simulation</span>")
except:
K=1
alphas = [1.0]
phases = [0]
thetas = [soi_theta]
self.label_status.setText("<span style=\" font-size:8pt; font-weight:600; color:#ff0000;\" >Improper multipath parameters</span>")
alphas = 10**(np.array(alphas)/10) * np.exp(1j*np.deg2rad(np.array(phases)))
noise_pow = 10**(-1*self.spinBox_snr_dB.value()/10)
# Generate the signal of interest
soi = np.random.normal(0,1,N) +1j* np.random.normal(0,1,N)
# Generate multichannel uncorrelated noise
noise = np.random.normal(0, np.sqrt(noise_pow), (M,N) ) +1j* np.random.normal(0, np.sqrt(noise_pow), (M,N) )
""" SNR debug display
pn = np.average(np.abs(noise**2))
ps = np.average(np.abs(soi**2))
logging.info("SNR: {:.2f}".format(10*np.log10(ps/pn)))
"""
self.axes_DOA.clear()
legend=[]
if self.checkBox_en_UCA.checkState():
#---------------- U C A-------------------
A = np.zeros((M, K), dtype=complex)
for k in range(K):
A[:,k] = np.exp(1j*2*np.pi*r*np.cos(np.radians(thetas[k]-np.arange(0,M,1)*(360)/M))) # UCA
soi_matrix = (np.outer( soi, np.inner(A, alphas))).T
# Create received signal
rec_signal = soi_matrix + noise
# Calulcate cross-correlation matrix
R = de.corr_matrix_estimate(rec_signal.T, imp="fast")
#R = forward_backward_avg(R)
# Generate array alignment vector
array_alignment = np.arange(0, M, 1) * d
scanning_vectors = de.gen_uca_scanning_vectors(M, r, self.thetas)
# DOA estimation
alias_highlight = False # Track thaht aliase regions are already shown
if self.checkBox_en_Bartlett.checkState():
Bartlett = de.DOA_Bartlett(R, scanning_vectors)
de.DOA_plot(Bartlett, self.thetas, log_scale_min = -50, axes=self.axes_DOA)
legend.append("UCA - Bartlett")
self.label_Bartlett_UCA_res.setText("{:.1f}".format(np.argmax(Bartlett)))
else:
self.label_Bartlett_UCA_res.setText("-")
if self.checkBox_en_Capon.checkState():
Capon = de.DOA_Capon(R, scanning_vectors)
de.DOA_plot(Capon, self.thetas, log_scale_min = -50, axes=self.axes_DOA)
legend.append("UCA - Capon")
self.label_Capon_UCA_res.setText("{:.1f}".format(np.argmax(Capon)))
else:
self.label_Capon_UCA_res.setText("-")
if self.checkBox_en_MEM.checkState():
MEM = de.DOA_MEM(R, scanning_vectors, column_select = 0)
de.DOA_plot(MEM, self.thetas, log_scale_min = -50, axes=self.axes_DOA)
legend.append("MEM")
self.label_MEM_UCA_res.setText("{:.1f}".format(np.argmax(MEM)))
else:
self.label_MEM_UCA_res.setText("-")
if self.checkBox_en_MUSIC.checkState():
MUSIC = de.DOA_MUSIC(R, scanning_vectors, signal_dimension = 1)
de.DOA_plot(MUSIC, self.thetas, log_scale_min = -50, axes=self.axes_DOA)
legend.append("MUSIC")
self.label_MUSIC_UCA_res.setText("{:.1f}".format(np.argmax(MUSIC)))
else:
self.label_MUSIC_UCA_res.setText("-")
if self.checkBox_en_ULA.checkState():
#---------------- U L A-------------------
# Prepare Array-response matrix
A = np.zeros((M, K), dtype=complex)
for k in range(K):
A[:,k] = np.exp(np.arange(0,M,1)*1j*2*np.pi*d*np.cos(np.deg2rad(thetas[k])))
soi_matrix = (np.outer( soi, np.inner(A, alphas))).T
# Create received signal
rec_signal = soi_matrix + noise
## R matrix calculation
R = de.corr_matrix_estimate(rec_signal.T, imp="fast")
if self.checkBox_en_FBavg.isChecked():
R = de.forward_backward_avg(R)
# Generate array alignment vector
array_alignment = np.arange(0, M, 1) * d
scanning_vectors = de.gen_ula_scanning_vectors(array_alignment, self.thetas)
# DOA estimation
alias_highlight = True # Track thaht aliase regions are already shown
if self.checkBox_en_Bartlett.checkState():
Bartlett = de.DOA_Bartlett(R, scanning_vectors)
de.DOA_plot(Bartlett, self.thetas, log_scale_min = -50, axes=self.axes_DOA, alias_highlight=alias_highlight, d=d)
legend.append("ULA - Bartlett")
alias_highlight = False
self.label_Bartlett_ULA_res.setText("{:.1f}".format(np.argmax(Bartlett[0:180])))
else:
self.label_Bartlett_ULA_res.setText("-")
if self.checkBox_en_Capon.checkState():
Capon = de.DOA_Capon(R, scanning_vectors)
de.DOA_plot(Capon, self.thetas, log_scale_min = -50, axes=self.axes_DOA, alias_highlight=alias_highlight, d=d)
legend.append("ULA - Capon")
alias_highlight = False
self.label_Capon_ULA_res.setText("{:.1f}".format(np.argmax(Capon[0:180])))
else:
self.label_Capon_ULA_res.setText("-")
if self.checkBox_en_MEM.checkState():
MEM = de.DOA_MEM(R, scanning_vectors, column_select = 0)
de.DOA_plot(MEM, self.thetas, log_scale_min = -50, axes=self.axes_DOA, alias_highlight=alias_highlight, d=d)
legend.append("ULA - MEM")
alias_highlight = False
self.label_MEM_ULA_res.setText("{:.1f}".format(np.argmax(MEM[0:180])))
else:
self.label_MEM_ULA_res.setText("-")
if self.checkBox_en_MUSIC.checkState():
MUSIC = de.DOA_MUSIC(R, scanning_vectors, signal_dimension = 1)
de.DOA_plot(MUSIC, self.thetas, log_scale_min = -50, axes=self.axes_DOA, alias_highlight=alias_highlight, d=d)
legend.append("ULA - MUSIC")
alias_highlight = False
self.label_MUSIC_ULA_res.setText("{:.1f}".format(np.argmax(MUSIC[0:180])))
else:
self.label_MUSIC_ULA_res.setText("-")
self.axes_DOA.legend(legend)
self.canvas_DOA.draw()