def ber_calculator(x_bits, Rb, nc, h, msk, alpha, snr):
signal, _ = gmsk_modulator(x_bits, Rb, nc, h, fs, alpha, msk)
# Transmitter definition
x_raw = Signal(fs, signal)
x_start = np.array([15, 0,
15]) # Start coordinate for the transmitter in m
v = np.array([1, 0, 0]) # Transmitter velocity in m/s
t = 0
fc = 436 * MHz
tx0 = Transmitter(x_start, v, t, fc, x_raw)
txs = []
txs.append(tx0)
# Phased array definition
mx = 4 # Number of sensors in direction X
my = 4 # Number of sensors in direction Y
origin = np.array([0, 0, 0]) # Axis origin
rx = PhasedArray(mx, my, txs[0].fc, origin)
# Simulation parameters
n = 1000 # Snapshots number
d = len(txs) # Number of signals/transmitters
simulation = Simulation(n, d, snr)
[s, x] = doa_samplesgen(txs, rx, simulation)
x_rs, _ = random_sampler(x, simulation.n)
doa = doaesprit_estimation(x_rs, rx)
x_beamformer = np.real(beamformer(x, rx, doa, fc))
x_det = gmsk_detector(x_beamformer, fs, Rb, nc)
n_errors = np.cumsum(np.abs(x_bits - x_det))[-1]
ber = n_errors / len(x_bits)
return ber
mx = M # Number of sensors in direction X my = M # Number of sensors in direction Y origin = np.array([0, 0, 0]) # Axis origin rx = PhasedArray(mx, my, txs[0].fc, origin) # Simulation parameters n = 124 # Snapshots number d = len(txs) # Number of signals/transmitters fs = 64 * MHz # Sampling frequency fc = rx.fc sampling_time = 5 * ms # Sampling time snr = 7 # SNR in dB simulation = Simulation(n, d, snr) [s, x] = doamusic_samples(txs, rx, simulation) np.degrees(doaesprit_estimation(x, rx)) #%% np.degrees(tx0.doa.el) # %% k = np.pi / rx.d m = x.shape[0] # Singular value decomposition of matrix "X" [u, s, v] = np.linalg.svd(x, full_matrices=True) # Get multiplicity 'q' of the minimun eigenvalue using histogram method aval_min = s[0] bins_n = 100 bins_aval = np.histogram(s, bins=bins_n) count = 0
# snr = 7
n = 1000 # Snapshots number
d = len(txs) # Number of signals/transmitters
simulation = Simulation(n, d, snr)
#%% Carrier Est vs SNR
error_array = np.zeros(snr_array.size)
fc_est = np.zeros(snr_array.size)
for i in range(snr_array.size):
n_error = 10
error = 0
snr = snr_array[i]
simulation = Simulation(n, d, snr)
_, x = doa_samplesgen(txs, rx, simulation)
x_rs, idx = random_sampler(x, simulation.n)
doa = doaesprit_estimation(x_rs, rx, [], simulation.d)
print(np.degrees(doa))
x_beamformer = beamformer(x, rx, doa, fc)
for j in range(n_error):
m = 30
x_rs, _ = random_sampler(x_beamformer, simulation.n, m)
fc_est[i] = carrieresprit_estimation(x_rs, fs1)
error = error + (1 / n_error) * (fc_rem - fc_est[i])**2
print(fc_est[i])
# print(error)
error_array[i] = error / (fc_rem)**2
print(error_array[i])
#%%
plt.figure(figsize=(16, 9), dpi=100)
plt.subplot(211)
36.74234614]) # Start coordinate for the transmitter in m v = np.array([1, 0, 0]) # Transmitter velocity in m/s t = 0 fc = 436 * MHz amp = 10 freq = 400 s = Sine_Wave(amp, freq) tx0 = Transmitter(x_start, v, t, fc, s) txs = [] txs.append(tx0) # Simulation parameters n = 1000 # Snapshots number d = len(txs) # Number of signals/transmitters fs = 64 * MHz # Sampling frequency fc = rx.fc sampling_time = 5 * ms # Sampling time snr = 7 # Signal-to-noise ratio in dB simulation = Simulation(n, d, fs, fc, sampling_time, snr) #%% [s, x] = doamusic_samples(txs, rx, simulation) doa = doaesprit_estimation(x, rx) s = beamformer(x, rx, doa, fc) #%% plt.figure() plt.plot(simulation.t, s, "o") plt.show()
error_el_music = np.zeros(snr_array.size)
error_az_esprit = np.zeros(snr_array.size)
error_el_esprit = np.zeros(snr_array.size)
n_error = 10
for i in range(snr_array.size):
for j in range(n_error):
snr = snr_array[i]
simulation = Simulation(n, d, fs, fc, sampling_time, snr)
[s, x] = doamusic_samples(txs, rx, simulation)
p_mu = doamusic_estimation(s, a)
idx = np.unravel_index(np.argmax(p_mu, axis=None), p_mu.shape)
el_music_est[i] = np.degrees(el_music[idx[0]])
az_music_est[i] = np.degrees(az_music[idx[1]])
[az_esprit_est[i],
el_esprit_est[i]] = np.degrees(doaesprit_estimation(x, rx))
error_az_music[i] = (error_az_music[i] + (1 / n_error) *
(az_music_est[i] - np.degrees(txs[0].doa.az))**2)
error_el_music[i] = (error_el_music[i] + (1 / n_error) *
(el_music_est[i] - np.degrees(txs[0].doa.el))**2)
error_az_esprit[i] = (
error_az_esprit[i] + (1 / n_error) *
(az_esprit_est[i] - np.degrees(txs[0].doa.az))**2)
error_el_esprit[i] = (
error_el_esprit[i] + (1 / n_error) *
(el_esprit_est[i] - np.degrees(txs[0].doa.el))**2)
# %%
plt.figure()
def image_array(i):
print(i)
t = t_array[i]
tx = Transmitter(x_start, v, t, fc, signal)
track[:, i] = np.array([tx.r[0], tx.r[2]])
_, x = doa_samplesgen(tx, rx, simulation)
# x, idx = random_sampler(x, simulation.n)
theta_est[i] = -np.degrees(doaesprit_estimation(x, rx, k, 1))
fig, ax = plt.subplots(2, 1, figsize=(16, 9), dpi=150, num=0)
img = Image.open("./images/satelite.png")
imagebox = OffsetImage(img, zoom=0.08)
ab = AnnotationBbox(imagebox, (track[0, i], track[1, i]), frameon=False)
ax[1].grid()
ax[0].add_artist(ab)
# ax[0].arrow(0,0,0,15,color='k',width=0.1)
ax[0].annotate("",
xy=(0, 0),
xytext=(0, 15),
arrowprops=dict(arrowstyle="<-", linewidth=3))
ax[0].annotate("",
xy=(0, 0),
xytext=(10, 0),
arrowprops=dict(arrowstyle="<-", linewidth=3))
ax[0].annotate("x", xy=(9.5, 0.5))
ax[0].annotate("z", xy=(0.5, 13.5))
ax[0].set_ylim((0, track[1, :].max() * (1 + 0.3)))
ax[0].set_xlim(-17, 17)
# line2, = ax[0].plot(track[0, i], track[1, i], "o")
arrow = ax[0].arrow(
0,
0,
track[0, i] * 0.9,
track[1, i] * 0.9,
width=0.15,
length_includes_head=True,
color="r",
)
# angle_plot = get_angle_plot(txs[i].doa.theta, 1)
theta_deg = tx.doa.theta * 180 / np.pi
theta_real_array[i] = theta_deg
if theta_deg < 0:
arc = matplotlib.patches.Arc((0, 0), 10, 10, 0, 90, 90 - theta_deg)
ax[0].add_patch(arc) # To display the angle arc
ax[0].annotate(r"$\theta=$%.2f°" % theta_deg, xy=(0.5, 2.5), size=15)
else:
arc = matplotlib.patches.Arc((0, 0), 10, 10, 0, 90 - theta_deg, 90)
ax[0].add_patch(arc) # To display the angle arc
ax[0].annotate(r"$\theta=$%.2f°" % theta_deg, xy=(-3.6, 2.5), size=15)
ax[1].set_ylim((-50, 50))
ax[1].set_xlim((0, 31))
ax[1].set_ylabel(r"$\theta$ [°]", size=15)
ax[1].set_xlabel(r"Time [s]", size=15)
ax[1].plot(t_array[0:i],
theta_est[0:i],
label="ESPRIT",
color="b",
linewidth=3)
ax[1].plot(
t_array[0:i],
theta_real_array[0:i],
label="Real",
linestyle="--",
color="r",
linewidth=2,
)
ax[1].legend()
ax[1].plot([t_array[i], t_array[i]], [0, theta_est[i]],
color="gray",
linestyle="--")
if theta_real_array[i] < 0:
ax[1].annotate(r"$\hat{\theta}=$%.2f°" % theta_est[i],
xy=(t_array[i] + 0.2, 1),
size=15)
else:
ax[1].annotate(r"$\hat{\theta}=$%.2f°" % theta_est[i],
xy=(t_array[i] - 2.5, -12),
size=15)
fig.canvas.draw() # draw the canvas, cache the renderer
image = np.frombuffer(fig.canvas.tostring_rgb(), dtype="uint8")
image = image.reshape(fig.canvas.get_width_height()[::-1] + (3, ))
return image
v = np.array([1, 0, 0]) # Transmitter velocity in m/s
fc = 436 * MHz
amp = 10
freq = 1300
fs = 48 * kHz # Sampling frequency
t_max = 5 * ms # Sampling time
sine = Sine_Wave(amp, freq, fs, t_max)
signal = Signal(sine.fs, sine.data)
n_time = 200
n_theta = 200
theta_array = np.empty(n_theta)
t_array = np.linspace(0, 30, n_time)
track = np.zeros((2, n_time))
lambda_c = c / rx.fc
k = 2 * np.pi / (lambda_c)
#%%
for i in range(n_time):
t = t_array[i]
tx = Transmitter(x_start, v, t, fc, signal)
track[:, i] = np.array([tx.r[0], tx.r[2]])
_, x = doa_samplesgen(tx, rx, simulation)
x, idx = random_sampler(x, simulation.n)
theta_array[i] = np.degrees(doaesprit_estimation(x, rx, k, 1))
#%%
plt.figure()
plt.plot(theta_array, t_array)
plt.show()
mx = M # Number of sensors in direction X
my = M # Number of sensors in direction Y
origin = np.array([0, 0, 0]) # Axis origin
rx = PhasedArray(mx, my, txs[0].fc, origin)
# Simulation parameters
n = 1000 # Snapshots number
d = len(txs) # Number of signals/transmitters
snr = 0 # SNR in dB
simulation = Simulation(n, d, snr)
[s, x] = doa_samplesgen(txs, rx, simulation)
# %%
# x_rs, _ = random_sampler(x, simulation.n)
doa = doaesprit_estimation(x, rx)
print(np.degrees(doa))
x_beamformer = beamformer(x, rx, doa, fc)
# %%
# %% Plot temporal
plt.figure()
plt.grid()
plt.plot(txs[0].x.t, txs[0].x.data)
plt.plot(txs[0].x.t, x_beamformer)
plt.xlabel("Tiempo [s]")
plt.ylabel("x(t)")
plt.show()
# %% Plot espectral
plt.figure()
