mirror of https://github.com/gnss-sdr/gnss-sdr
141 lines
5.1 KiB
Python
141 lines
5.1 KiB
Python
"""
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plotKalman.py
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plotKalman (channelNr, trackResults, settings)
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This function plots the tracking results for the given channel list.
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Irene Pérez Riega, 2023. iperrie@inta.es
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Args:
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channelList - list of channels to be plotted.
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trackResults - tracking results from the tracking function.
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settings - receiver settings.
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Modifiable in the file:
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fig_path - Path where doppler plots will be save
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-----------------------------------------------------------------------------
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GNSS-SDR is a Global Navigation Satellite System software-defined receiver.
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This file is part of GNSS-SDR.
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Copyright (C) 2022 (see AUTHORS file for a list of contributors)
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SPDX-License-Identifier: GPL-3.0-or-later
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-----------------------------------------------------------------------------
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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import os
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def plotKalman(channelNr, trackResults, settings):
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# ---------- CHANGE HERE:
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fig_path = '/home/labnav/Desktop/TEST_IRENE/PLOTS/PlotKalman'
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if not os.path.exists(fig_path):
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os.makedirs(fig_path)
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# Protection - if the list contains incorrect channel numbers
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channelNr = np.intersect1d(channelNr,
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np.arange(1, settings['numberOfChannels'] + 1))
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for channelNr in channelNr:
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time_start = settings['timeStartInSeconds']
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time_axis_in_seconds = np.arange(1, settings['msToProcess']+1)/1000
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# Plot all figures
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plt.figure(figsize=(1920 / 100, 1080 / 100))
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plt.clf()
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plt.gcf().canvas.set_window_title(
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f'Channel {channelNr} (PRN '
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f'{str(trackResults[channelNr-1]["PRN"][-2])}) results')
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plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.1,
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hspace=0.4, wspace=0.4)
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plt.tight_layout()
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# Row 1
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# ----- CNo for signal -----------------------------------------------
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# Measure of the ratio between carrier signal power and noise power
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plt.subplot(4, 2, 1)
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plt.plot(time_axis_in_seconds,
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trackResults[channelNr-1]['CNo'][:settings['msToProcess']],
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'b')
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plt.grid()
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plt.axis('tight')
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plt.xlabel('Time (s)')
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plt.ylabel('CNo (dB-Hz)')
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plt.title('Carrier to Noise Ratio', fontweight='bold')
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# ----- PLL discriminator filtered -----------------------------------
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plt.subplot(4, 2, 2)
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plt.plot(time_axis_in_seconds,
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trackResults[channelNr-1]['state1']
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[:settings['msToProcess']], 'b')
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plt.grid()
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plt.axis('tight')
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plt.xlim([time_start, time_axis_in_seconds[-1]])
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plt.xlabel('Time (s)')
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plt.ylabel('Phase Amplitude')
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plt.title('Filtered Carrier Phase', fontweight='bold')
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# Row 2
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# ----- Carrier Frequency --------------------------------------------
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# Filtered carrier frequency of (transmitted by a satellite)
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# for a specific channel
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plt.subplot(4, 2, 3)
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plt.plot(time_axis_in_seconds[1:],
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trackResults[channelNr-1]['state2']
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[1:settings['msToProcess']], color=[0.42, 0.25, 0.39])
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plt.grid()
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plt.axis('auto')
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plt.xlim(time_start, time_axis_in_seconds[-1])
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plt.xlabel('Time (s)')
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plt.ylabel('Freq (Hz)')
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plt.title('Filtered Carrier Frequency', fontweight='bold')
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# ----- Carrier Frequency Rate ---------------------------------------
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plt.subplot(4, 2, 4)
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plt.plot(time_axis_in_seconds[1:],
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trackResults[channelNr-1]['state3']
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[1:settings['msToProcess']], color=[0.42, 0.25, 0.39])
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plt.grid()
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plt.axis('auto')
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plt.xlim(time_start, time_axis_in_seconds[-1])
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plt.xlabel('Time (s)')
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plt.ylabel('Freq (Hz)')
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plt.title('Filtered Carrier Frequency Rate', fontweight='bold')
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# Row 3
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# ----- PLL discriminator unfiltered----------------------------------
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plt.subplot(4, 2, (5,6))
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plt.plot(time_axis_in_seconds,
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trackResults[channelNr-1]['innovation'], 'r')
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plt.grid()
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plt.axis('auto')
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plt.xlim(time_start, time_axis_in_seconds[-1])
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plt.xlabel('Time (s)')
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plt.ylabel('Amplitude')
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plt.title('Raw PLL discriminator (Innovation)',fontweight='bold')
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# Row 4
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# ----- PLL discriminator covariance ---------------------------------
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plt.subplot(4, 2, (7,8))
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plt.plot(time_axis_in_seconds,
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trackResults[channelNr-1]['r_noise_cov'], 'r')
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plt.grid()
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plt.axis('auto')
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plt.xlim(time_start, time_axis_in_seconds[-1])
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plt.xlabel('Time (s)')
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plt.ylabel('Variance')
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plt.title('Estimated Noise Variance', fontweight='bold')
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plt.tight_layout()
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plt.savefig(os.path.join(fig_path,
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f'kalman_ch{channelNr}_PRN_'
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f'{trackResults[channelNr - 1]["PRN"][-1]}'
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f'.png'))
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plt.show()
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