mirror of
https://github.com/gnss-sdr/gnss-sdr
synced 2026-07-02 09:28:50 +00:00
minhaj
43bceb52ff
The KF tracking dump reader now follows the parameters dumped by kf_tracking.cc lib/gps_l1_ca_kf_read_tracking_dump.py: - Rewrite using the same _RECORD_FORMAT / _FIELD_NAMES architecture as dll_pll_veml_read_tracking_dump.py: one struct.unpack per record and EOF handling, dropping the v1..v22 / bytes_shift / seek. gps_l1_ca_kf_plot_sample.py: - map innovation -> carr_error; drop r_noise_cov lib/plotKalman.py, lib/plotTracking.py: - Skip the "Estimated Noise Variance" panel when r_noise_cov is absent. - Replace fig.canvas.set_window_title() with fig.canvas.manager.set_window_title(). The canvas method was deprecated in matplotlib 3.4 and is gone in current matplotlib (3.6.3 here), where it raised AttributeError. Ref: https://github.com/raysect/source/issues/383 Signed-off-by: minhaj <minhaj.sixbyte@gmail.com>
108 lines
4.0 KiB
Python
108 lines
4.0 KiB
Python
"""
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gps_l1_ca_kf_plot_sample.py
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Reads a GPS L1 C/A Kalman-filter tracking dump binary file
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(GPS_L1_CA_KF_Tracking) and plots some internal tracking and Kalman-filter
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variables.
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Irene Pérez Riega, 2023. iperrie@inta.es
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Minhaj Uddin Ahmad, 2026. mahmad12@crimson.ua.edu
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Modifiable in the file:
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samplingFreq - Sampling frequency [Hz]
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channels - Number of channels
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first_channel - Number of the first channel
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code_period - Code period [s]
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path - Path to folder which contains the tracking dump files
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fig_path - Path where the plots will be saved
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file_prefix - Fixed part of the tracking dump file names
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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 os
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import numpy as np
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from lib.gps_l1_ca_kf_read_tracking_dump import gps_l1_ca_kf_read_tracking_dump
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from lib.plotTracking import plotTracking
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from lib.plotKalman import plotKalman
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GNSS_tracking = []
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trackResults = []
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kalmanResults = []
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# ---------- CHANGE HERE:
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samplingFreq = 4000000 # must match SignalSource.sampling_frequency in the .conf
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channels = 5
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first_channel = 0
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code_period = 0.001
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path = '../../../out/'
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fig_path = '../../../PLOTS/KF_Tracking'
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file_prefix = 'track_ch'
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for N in range(1, channels + 1):
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tracking_log_path = os.path.join(path,
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f'{file_prefix}{N-1+first_channel}.dat')
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GNSS_tracking.append(gps_l1_ca_kf_read_tracking_dump(tracking_log_path))
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for N in range(1, channels + 1):
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trackResult = {
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'status': 'T', # fake track
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'codeFreq': np.copy(GNSS_tracking[N-1]["code_freq_hz"]),
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'carrFreq': np.copy(GNSS_tracking[N-1]["carrier_doppler_hz"]),
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'carrFreqRate':
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np.copy(GNSS_tracking[N-1]["carrier_doppler_rate_hz2"]),
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'dllDiscr': np.copy(GNSS_tracking[N-1]["code_error"]),
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'dllDiscrFilt': np.copy(GNSS_tracking[N-1]["code_nco"]),
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'pllDiscr': np.copy(GNSS_tracking[N-1]["carr_error"]),
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'pllDiscrFilt': np.copy(GNSS_tracking[N-1]["carr_nco"]),
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'I_P': np.copy(GNSS_tracking[N-1]["prompt_I"]),
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'Q_P': np.copy(GNSS_tracking[N-1]["prompt_Q"]),
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'I_E': np.copy(GNSS_tracking[N-1]["E"]),
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'I_L': np.copy(GNSS_tracking[N-1]["L"]),
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'Q_E': np.zeros(len(GNSS_tracking[N-1]["E"])),
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'Q_L': np.zeros(len(GNSS_tracking[N-1]["L"])),
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'PRN': np.copy(GNSS_tracking[N-1]["PRN"]),
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'CNo': np.copy(GNSS_tracking[N-1]["CN0_SNV_dB_Hz"]),
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'prn_start_time_s':
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np.copy(GNSS_tracking[N-1]["PRN_start_sample"]) / samplingFreq
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}
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# Kalman-filter internals. The KF dump stores the carrier discriminator
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# (carr_error -> innovation) and the filter states, but no measurement
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# noise covariance, so 'r_noise_cov' is intentionally left out (plotKalman
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# skips that panel when it is absent).
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kalmanResult = {
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'PRN': np.copy(GNSS_tracking[N-1]["PRN"]),
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'innovation': np.copy(GNSS_tracking[N-1]["carr_error"]),
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'state1': np.copy(GNSS_tracking[N-1]["carr_nco"]),
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'state2': np.copy(GNSS_tracking[N-1]["carrier_doppler_hz"]),
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'state3': np.copy(GNSS_tracking[N-1]["carrier_doppler_rate_hz2"]),
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'CNo': np.copy(GNSS_tracking[N-1]["CN0_SNV_dB_Hz"])
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}
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trackResults.append(trackResult)
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kalmanResults.append(kalmanResult)
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settings = {
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'numberOfChannels': channels,
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'msToProcess': len(GNSS_tracking[N-1]['E']),
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'codePeriod': code_period,
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'timeStartInSeconds': 0,
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'fig_path': fig_path,
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'show': False # set True to display the figures interactively
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}
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# Create and save graphics as PNG
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plotTracking(N, trackResults, settings)
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plotKalman(N, kalmanResults, settings)
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