1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
|
"""
plotKalman.py
plotKalman (channelNr, trackResults, settings)
This function plots the tracking results for the given channel list.
Irene Pérez Riega, 2023. iperrie@inta.es
Args:
channelList - list of channels to be plotted.
trackResults - tracking results from the tracking function.
settings - receiver settings.
Modifiable in the file:
fig_path - Path where doppler plots will be save
-----------------------------------------------------------------------------
GNSS-SDR is a Global Navigation Satellite System software-defined receiver.
This file is part of GNSS-SDR.
Copyright (C) 2022 (see AUTHORS file for a list of contributors)
SPDX-License-Identifier: GPL-3.0-or-later
-----------------------------------------------------------------------------
"""
import matplotlib.pyplot as plt
import numpy as np
import os
def plotKalman(channelNr, trackResults, settings):
# ---------- CHANGE HERE:
fig_path = '/home/labnav/Desktop/TEST_IRENE/PLOTS/PlotKalman'
if not os.path.exists(fig_path):
os.makedirs(fig_path)
# Protection - if the list contains incorrect channel numbers
channelNr = np.intersect1d(channelNr,
np.arange(1, settings['numberOfChannels'] + 1))
for channelNr in channelNr:
time_start = settings['timeStartInSeconds']
time_axis_in_seconds = np.arange(1, settings['msToProcess']+1)/1000
# Plot all figures
plt.figure(figsize=(1920 / 100, 1080 / 100))
plt.clf()
plt.gcf().canvas.set_window_title(
f'Channel {channelNr} (PRN '
f'{str(trackResults[channelNr-1]["PRN"][-2])}) results')
plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.1,
hspace=0.4, wspace=0.4)
plt.tight_layout()
# Row 1
# ----- CNo for signal -----------------------------------------------
# Measure of the ratio between carrier signal power and noise power
plt.subplot(4, 2, 1)
plt.plot(time_axis_in_seconds,
trackResults[channelNr-1]['CNo'][:settings['msToProcess']],
'b')
plt.grid()
plt.axis('tight')
plt.xlabel('Time (s)')
plt.ylabel('CNo (dB-Hz)')
plt.title('Carrier to Noise Ratio', fontweight='bold')
# ----- PLL discriminator filtered -----------------------------------
plt.subplot(4, 2, 2)
plt.plot(time_axis_in_seconds,
trackResults[channelNr-1]['state1']
[:settings['msToProcess']], 'b')
plt.grid()
plt.axis('tight')
plt.xlim([time_start, time_axis_in_seconds[-1]])
plt.xlabel('Time (s)')
plt.ylabel('Phase Amplitude')
plt.title('Filtered Carrier Phase', fontweight='bold')
# Row 2
# ----- Carrier Frequency --------------------------------------------
# Filtered carrier frequency of (transmitted by a satellite)
# for a specific channel
plt.subplot(4, 2, 3)
plt.plot(time_axis_in_seconds[1:],
trackResults[channelNr-1]['state2']
[1:settings['msToProcess']], color=[0.42, 0.25, 0.39])
plt.grid()
plt.axis('auto')
plt.xlim(time_start, time_axis_in_seconds[-1])
plt.xlabel('Time (s)')
plt.ylabel('Freq (Hz)')
plt.title('Filtered Carrier Frequency', fontweight='bold')
# ----- Carrier Frequency Rate ---------------------------------------
plt.subplot(4, 2, 4)
plt.plot(time_axis_in_seconds[1:],
trackResults[channelNr-1]['state3']
[1:settings['msToProcess']], color=[0.42, 0.25, 0.39])
plt.grid()
plt.axis('auto')
plt.xlim(time_start, time_axis_in_seconds[-1])
plt.xlabel('Time (s)')
plt.ylabel('Freq (Hz)')
plt.title('Filtered Carrier Frequency Rate', fontweight='bold')
# Row 3
# ----- PLL discriminator unfiltered----------------------------------
plt.subplot(4, 2, (5,6))
plt.plot(time_axis_in_seconds,
trackResults[channelNr-1]['innovation'], 'r')
plt.grid()
plt.axis('auto')
plt.xlim(time_start, time_axis_in_seconds[-1])
plt.xlabel('Time (s)')
plt.ylabel('Amplitude')
plt.title('Raw PLL discriminator (Innovation)',fontweight='bold')
# Row 4
# ----- PLL discriminator covariance ---------------------------------
plt.subplot(4, 2, (7,8))
plt.plot(time_axis_in_seconds,
trackResults[channelNr-1]['r_noise_cov'], 'r')
plt.grid()
plt.axis('auto')
plt.xlim(time_start, time_axis_in_seconds[-1])
plt.xlabel('Time (s)')
plt.ylabel('Variance')
plt.title('Estimated Noise Variance', fontweight='bold')
plt.tight_layout()
plt.savefig(os.path.join(fig_path,
f'kalman_ch{channelNr}_PRN_'
f'{trackResults[channelNr - 1]["PRN"][-1]}'
f'.png'))
plt.show()
|
