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1843061376
Fix error in scaling of population high frequency vibration metrics Add histograms for delta angle and velocity bias data Fix variable descriptions
584 lines
31 KiB
Python
584 lines
31 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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import argparse
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import os
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import numpy as np
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import matplotlib.pyplot as plt
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"""
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Performs a composite analysis of ekf log analysis meta data for all .ulg.csv files in the specified directory
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Generates and saves histogram plots for the meta data in population_data.pdf
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Generates and saves population summary data in population_data.csv
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"""
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parser = argparse.ArgumentParser(description='Perform a composite analysis of ekf log analysis meta data for all .ulg.csv files in the specified directory')
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parser.add_argument("directory_path")
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def is_valid_directory(parser, arg):
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if os.path.isdir(arg):
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# Directory exists so return the directory
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return arg
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else:
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parser.error('The directory {} does not exist'.format(arg))
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args = parser.parse_args()
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metadata_directory = args.directory_path
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# Run the metadata analsyis tool to generate population statistics
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# Loop through the csv files in the directory and load the metadata into a nested dictionary
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print("\n"+"analysing all .ulog.csv files in "+metadata_directory)
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population_data = {}
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for filename in os.listdir(metadata_directory):
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if filename.endswith(".mdat.csv"):
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print("loading "+filename)
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# get the dictionary of fail and warning test thresholds from a csv file
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file = open(metadata_directory+"/"+filename)
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single_log_data = { } # meta data dictionary for a single log
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for line in file:
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x = line.split(",")
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a=x[0]
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b=x[1]
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c=x[2]
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try:
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single_log_data[a]=float(b)
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except:
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single_log_data[a]=b
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file.close()
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population_data[filename]=single_log_data
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# # print out the check levels
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# print('\n'+'The following metadata loaded from '+filename+' were used'+'\n')
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# val = population_data.get(filename, {}).get('imu_hfdang_mean')
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# print(val)
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# Open pdf file for plotting
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from matplotlib.backends.backend_pdf import PdfPages
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output_plot_filename = "population_data.pdf"
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pp = PdfPages(metadata_directory+"/"+output_plot_filename)
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# get statistics for the population
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population_results = {
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'master_warning_pct':[float('NaN'),'Percentage of logs with warnings'],
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'master_fail_pct':[float('NaN'),'Percentage of logs with fails'],
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'mag_warning_pct':[float('NaN'),'Percentage of logs with magnetometer sensor warnings'],
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'mag_fail_pct':[float('NaN'),'Percentage of logs with magnetometer sensor fails'],
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'yaw_warning_pct':[float('NaN'),'Percentage of logs with yaw sensor warnings'],
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'yaw_fail_pct':[float('NaN'),'Percentage of logs with yaw sensor fails'],
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'vel_warning_pct':[float('NaN'),'Percentage of logs with velocity sensor warnings'],
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'vel_fail_pct':[float('NaN'),'Percentage of logs with velocity sensor fails'],
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'pos_warning_pct':[float('NaN'),'Percentage of logs with position sensor warnings'],
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'pos_fail_pct':[float('NaN'),'Percentage of logs with position sensor fails'],
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'hgt_warning_pct':[float('NaN'),'Percentage of logs with height sensor warnings'],
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'hgt_fail_pct':[float('NaN'),'Percentage of logs with height sensor fails'],
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'hagl_warning_pct':[float('NaN'),'Percentage of logs with height above ground sensor warnings'],
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'hagl_fail_pct':[float('NaN'),'Percentage of logs with height above ground sensor fails'],
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'tas_warning_pct':[float('NaN'),'Percentage of logs with airspeed sensor warnings'],
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'tas_fail_pct':[float('NaN'),'Percentage of logs with airspeed ground sensor fails'],
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'mag_test_max_avg':[float('NaN'),'The mean of the maximum in-flight values of the magnetic field sensor innovation consistency test ratio'],
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'mag_test_mean_avg':[float('NaN'),'The mean of the mean in-flight value of the magnetic field sensor innovation consistency test ratio'],
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'vel_test_max_avg':[float('NaN'),'The mean of the maximum in-flight values of the velocity sensor innovation consistency test ratio'],
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'vel_test_mean_avg':[float('NaN'),'The mean of the mean in-flight value of the velocity sensor innovation consistency test ratio'],
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'pos_test_max_avg':[float('NaN'),'The mean of the maximum in-flight values of the position sensor innovation consistency test ratio'],
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'pos_test_mean_avg':[float('NaN'),'The mean of the mean in-flight value of the position sensor innovation consistency test ratio'],
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'hgt_test_max_avg':[float('NaN'),'The mean of the maximum in-flight values of the height sensor innovation consistency test ratio'],
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'hgt_test_mean_avg':[float('NaN'),'The mean of the mean in-flight value of the height sensor innovation consistency test ratio'],
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'tas_test_max_avg':[float('NaN'),'The mean of the maximum in-flight values of the airspeed sensor innovation consistency test ratio'],
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'tas_test_mean_avg':[float('NaN'),'The mean of the mean in-flight value of the airspeed sensor innovation consistency test ratio'],
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'hagl_test_max_avg':[float('NaN'),'The mean of the maximum in-flight values of the height above ground sensor innovation consistency test ratio'],
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'hagl_test_mean_avg':[float('NaN'),'The mean of the mean in-flight value of the height above ground sensor innovation consistency test ratio'],
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'ofx_fail_pct_avg':[float('NaN'),'The mean percentage of innovation test fails for the X axis optical flow sensor'],
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'ofy_fail_pct_avg':[float('NaN'),'The mean percentage of innovation test fails for the Y axis optical flow sensor'],
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'imu_coning_max_avg':[float('NaN'),'The mean of the maximum in-flight values of the IMU delta angle coning vibration level (mrad)'],
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'imu_coning_mean_avg':[float('NaN'),'The mean of the mean in-flight value of the IMU delta angle coning vibration level (mrad)'],
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'imu_hfdang_max_avg':[float('NaN'),'The mean of the maximum in-flight values of the IMU high frequency delta angle vibration level (mrad)'],
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'imu_hfdang_mean_avg':[float('NaN'),'The mean of the mean in-flight value of the IMU delta high frequency delta angle vibration level (mrad)'],
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'imu_hfdvel_max_avg':[float('NaN'),'The mean of the maximum in-flight values of the IMU high frequency delta velocity vibration level (m/s)'],
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'imu_hfdvel_mean_avg':[float('NaN'),'The mean of the mean in-flight value of the IMU delta high frequency delta velocity vibration level (m/s)'],
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'obs_ang_median_avg':[float('NaN'),'The mean of the median in-flight value of the output observer angular tracking error magnitude (mrad)'],
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'obs_vel_median_avg':[float('NaN'),'The mean of the median in-flight value of the output observer velocity tracking error magnitude (m/s)'],
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'obs_pos_median_avg':[float('NaN'),'The mean of the median in-flight value of the output observer position tracking error magnitude (m)'],
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}
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# get population summary statistics
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found_keys = population_data.keys()
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# master status
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result = [population_data[k].get('master_status') for k in found_keys]
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population_results['master_warning_pct'][0] = 100.0 * result.count('Warning') / len(result)
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population_results['master_fail_pct'][0] = 100.0 * result.count('Fail') / len(result)
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# magnetometer sensor
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result = [population_data[k].get('mag_sensor_status') for k in found_keys]
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population_results['mag_warning_pct'][0] = 100.0 * result.count('Warning') / len(result)
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population_results['mag_fail_pct'][0] = 100.0 * result.count('Fail') / len(result)
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# yaw sensor
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result = [population_data[k].get('yaw_sensor_status') for k in found_keys]
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population_results['yaw_warning_pct'][0] = 100.0 * result.count('Warning') / len(result)
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population_results['yaw_fail_pct'][0] = 100.0 * result.count('Fail') / len(result)
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# velocity sensor
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result = [population_data[k].get('vel_sensor_status') for k in found_keys]
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population_results['vel_warning_pct'][0] = 100.0 * result.count('Warning') / len(result)
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population_results['vel_fail_pct'][0] = 100.0 * result.count('Fail') / len(result)
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# position sensor
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result = [population_data[k].get('pos_sensor_status') for k in found_keys]
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population_results['pos_warning_pct'][0] = 100.0 * result.count('Warning') / len(result)
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population_results['pos_fail_pct'][0] = 100.0 * result.count('Fail') / len(result)
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# height sensor
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result = [population_data[k].get('hgt_sensor_status') for k in found_keys]
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population_results['hgt_warning_pct'][0] = 100.0 * result.count('Warning') / len(result)
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population_results['hgt_fail_pct'][0] = 100.0 * result.count('Fail') / len(result)
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# height above ground sensor
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result = [population_data[k].get('hagl_sensor_status') for k in found_keys]
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population_results['hagl_warning_pct'][0] = 100.0 * result.count('Warning') / len(result)
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population_results['hagl_fail_pct'][0] = 100.0 * result.count('Fail') / len(result)
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# height above ground sensor
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result = [population_data[k].get('tas_sensor_status') for k in found_keys]
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population_results['tas_warning_pct'][0] = 100.0 * result.count('Warning') / len(result)
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population_results['tas_fail_pct'][0] = 100.0 * result.count('Fail') / len(result)
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# Mean and max innovation test levels
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# Magnetometer
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temp = np.asarray([population_data[k].get('mag_test_max') for k in found_keys])
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result1 = temp[np.isfinite(temp)]
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temp = np.asarray([population_data[k].get('mag_test_mean') for k in found_keys])
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result2 = temp[np.isfinite(temp)]
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if (len(result1) > 0 and len(result2) > 0):
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population_results['mag_test_max_avg'][0] = np.mean(result1)
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population_results['mag_test_mean_avg'][0] = np.mean(result2)
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plt.figure(1,figsize=(20,13))
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plt.subplot(2,1,1)
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plt.hist(result1)
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plt.title("Gaussian Histogram - Magnetometer Innovation Test Ratio Maximum")
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plt.xlabel("mag_test_max")
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plt.ylabel("Frequency")
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plt.subplot(2,1,2)
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plt.hist(result2)
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plt.title("Gaussian Histogram - Magnetometer Innovation Test Ratio Mean")
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plt.xlabel("mag_test_mean")
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plt.ylabel("Frequency")
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pp.savefig()
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plt.close(1)
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# Velocity Sensor (GPS)
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temp = np.asarray([population_data[k].get('vel_test_max') for k in found_keys])
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result1 = temp[np.isfinite(temp)]
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temp = np.asarray([population_data[k].get('vel_test_mean') for k in found_keys])
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result2 = temp[np.isfinite(temp)]
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if (len(result1) > 0 and len(result2) > 0):
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population_results['vel_test_max_avg'][0] = np.mean(result1)
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population_results['vel_test_mean_avg'][0] = np.mean(result2)
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plt.figure(2,figsize=(20,13))
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plt.subplot(2,1,1)
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plt.hist(result1)
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plt.title("Gaussian Histogram - Velocity Innovation Test Ratio Maximum")
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plt.xlabel("vel_test_max")
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plt.ylabel("Frequency")
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plt.subplot(2,1,2)
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plt.hist(result2)
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plt.title("Gaussian Histogram - Velocity Innovation Test Ratio Mean")
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plt.xlabel("vel_test_mean")
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plt.ylabel("Frequency")
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pp.savefig()
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plt.close(2)
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# Position Sensor (GPS or external vision)
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temp = np.asarray([population_data[k].get('pos_test_max') for k in found_keys])
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result1 = temp[np.isfinite(temp)]
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temp = np.asarray([population_data[k].get('pos_test_mean') for k in found_keys])
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result2 = temp[np.isfinite(temp)]
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if (len(result1) > 0 and len(result2) > 0):
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population_results['pos_test_max_avg'][0] = np.mean(result1)
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population_results['pos_test_mean_avg'][0] = np.mean(result2)
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plt.figure(3,figsize=(20,13))
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plt.subplot(2,1,1)
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plt.hist(result1)
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plt.title("Gaussian Histogram - Position Innovation Test Ratio Maximum")
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plt.xlabel("pos_test_max")
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plt.ylabel("Frequency")
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plt.subplot(2,1,2)
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plt.hist(result2)
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plt.title("Gaussian Histogram - Position Innovation Test Ratio Mean")
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plt.xlabel("pos_test_mean")
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plt.ylabel("Frequency")
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pp.savefig()
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plt.close(3)
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# Height Sensor
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temp = np.asarray([population_data[k].get('hgt_test_max') for k in found_keys])
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result1 = temp[np.isfinite(temp)]
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temp = np.asarray([population_data[k].get('hgt_test_mean') for k in found_keys])
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result2 = temp[np.isfinite(temp)]
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if (len(result1) > 0 and len(result2) > 0):
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population_results['hgt_test_max_avg'][0] = np.mean(result1)
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population_results['hgt_test_mean_avg'][0] = np.mean(result2)
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plt.figure(4,figsize=(20,13))
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plt.subplot(2,1,1)
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plt.hist(result1)
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plt.title("Gaussian Histogram - Height Innovation Test Ratio Maximum")
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plt.xlabel("pos_test_max")
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plt.ylabel("Frequency")
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plt.subplot(2,1,2)
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plt.hist(result2)
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plt.title("Gaussian Histogram - Height Innovation Test Ratio Mean")
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plt.xlabel("pos_test_mean")
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plt.ylabel("Frequency")
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pp.savefig()
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plt.close(4)
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# Airspeed Sensor
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temp = np.asarray([population_data[k].get('tas_test_max') for k in found_keys])
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result1 = temp[np.isfinite(temp)]
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temp = np.asarray([population_data[k].get('tas_test_mean') for k in found_keys])
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result2 = temp[np.isfinite(temp)]
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if (len(result1) > 0 and len(result2) > 0):
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population_results['tas_test_max_avg'][0] = np.mean(result1)
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population_results['tas_test_mean_avg'][0] = np.mean(result2)
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plt.figure(5,figsize=(20,13))
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plt.subplot(2,1,1)
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plt.hist(result1)
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plt.title("Gaussian Histogram - Airspeed Innovation Test Ratio Maximum")
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plt.xlabel("tas_test_max")
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plt.ylabel("Frequency")
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plt.subplot(2,1,2)
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plt.hist(result2)
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plt.title("Gaussian Histogram - Airspeed Innovation Test Ratio Mean")
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plt.xlabel("tas_test_mean")
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plt.ylabel("Frequency")
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pp.savefig()
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plt.close(5)
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# Height Above Ground Sensor
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temp = np.asarray([population_data[k].get('hagl_test_max') for k in found_keys])
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result1 = temp[np.isfinite(temp)]
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temp = np.asarray([population_data[k].get('hagl_test_mean') for k in found_keys])
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result2 = temp[np.isfinite(temp)]
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if (len(result1) > 0 and len(result2) > 0):
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population_results['hagl_test_max_avg'][0] = np.mean(result1)
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population_results['hagl_test_mean_avg'][0] = np.mean(result2)
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plt.figure(6,figsize=(20,13))
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plt.subplot(2,1,1)
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plt.hist(result1)
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plt.title("Gaussian Histogram - HAGL Innovation Test Ratio Maximum")
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plt.xlabel("hagl_test_max")
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plt.ylabel("Frequency")
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plt.subplot(2,1,2)
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plt.hist(result2)
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plt.title("Gaussian Histogram - HAGL Innovation Test Ratio Mean")
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plt.xlabel("hagl_test_mean")
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plt.ylabel("Frequency")
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pp.savefig()
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plt.close(6)
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# Optical Flow Sensor
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temp = np.asarray([population_data[k].get('ofx_fail_percentage') for k in found_keys])
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result1 = temp[np.isfinite(temp)]
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temp = np.asarray([population_data[k].get('ofy_fail_percentage') for k in found_keys])
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result2 = temp[np.isfinite(temp)]
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if (len(result1) > 0 and len(result2) > 0):
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population_results['ofx_fail_pct_avg'][0] = np.mean(result1)
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population_results['ofy_fail_pct_avg'][0] = np.mean(result2)
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plt.figure(7,figsize=(20,13))
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plt.subplot(2,1,1)
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plt.hist(result1)
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plt.title("Gaussian Histogram - Optical Flow X Axis Fail Percentage")
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plt.xlabel("ofx_fail_percentage")
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plt.ylabel("Frequency")
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plt.subplot(2,1,2)
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plt.hist(result2)
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plt.title("Gaussian Histogram - Optical Flow Y Axis Fail Percentage")
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plt.xlabel("ofy_fail_percentage")
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plt.ylabel("Frequency")
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pp.savefig()
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plt.close(7)
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# IMU coning vibration levels
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temp = np.asarray([population_data[k].get('imu_coning_peak') for k in found_keys])
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result1 = 1000.0 * temp[np.isfinite(temp)]
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temp = np.asarray([population_data[k].get('imu_coning_mean') for k in found_keys])
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result2 = 1000.0 * temp[np.isfinite(temp)]
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if (len(result1) > 0 and len(result2) > 0):
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population_results['imu_coning_max_avg'][0] = np.mean(result1)
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population_results['imu_coning_mean_avg'][0] = np.mean(result2)
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plt.figure(8,figsize=(20,13))
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plt.subplot(2,1,1)
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plt.hist(result1)
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plt.title("Gaussian Histogram - IMU Coning Vibration Peak")
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plt.xlabel("imu_coning_max (mrad)")
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plt.ylabel("Frequency")
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plt.subplot(2,1,2)
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plt.hist(result2)
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plt.title("Gaussian Histogram - IMU Coning Vibration Mean")
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plt.xlabel("imu_coning_mean (mrad)")
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plt.ylabel("Frequency")
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pp.savefig()
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plt.close(8)
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# IMU high frequency delta angle vibration levels
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temp = np.asarray([population_data[k].get('imu_hfdang_peak') for k in found_keys])
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result1 = 1000.0 * temp[np.isfinite(temp)]
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temp = np.asarray([population_data[k].get('imu_hfdang_mean') for k in found_keys])
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result2 = 1000.0 * temp[np.isfinite(temp)]
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if (len(result1) > 0 and len(result2) > 0):
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population_results['imu_hfdang_max_avg'][0] = np.mean(result1)
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population_results['imu_hfdang_mean_avg'][0] = np.mean(result2)
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plt.figure(9,figsize=(20,13))
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plt.subplot(2,1,1)
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plt.hist(result1)
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plt.title("Gaussian Histogram - IMU HF Delta Angle Vibration Peak")
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plt.xlabel("imu_hfdang_max (mrad)")
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plt.ylabel("Frequency")
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plt.subplot(2,1,2)
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plt.hist(result2)
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plt.title("Gaussian Histogram - IMU HF Delta Angle Vibration Mean")
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plt.xlabel("imu_hfdang_mean (mrad)")
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plt.ylabel("Frequency")
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pp.savefig()
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plt.close(9)
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# IMU high frequency delta velocity vibration levels
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temp = np.asarray([population_data[k].get('imu_hfdvel_peak') for k in found_keys])
|
|
result1 = temp[np.isfinite(temp)]
|
|
temp = np.asarray([population_data[k].get('imu_hfdvel_mean') for k in found_keys])
|
|
result2 = temp[np.isfinite(temp)]
|
|
|
|
if (len(result1) > 0 and len(result2) > 0):
|
|
population_results['imu_hfdvel_max_avg'][0] = np.mean(result1)
|
|
population_results['imu_hfdvel_mean_avg'][0] = np.mean(result2)
|
|
|
|
plt.figure(10,figsize=(20,13))
|
|
|
|
plt.subplot(2,1,1)
|
|
plt.hist(result1)
|
|
plt.title("Gaussian Histogram - IMU HF Delta Velocity Vibration Peak")
|
|
plt.xlabel("imu_hfdvel_max (m/s)")
|
|
plt.ylabel("Frequency")
|
|
|
|
plt.subplot(2,1,2)
|
|
plt.hist(result2)
|
|
plt.title("Gaussian Histogram - IMU HF Delta Velocity Vibration Mean")
|
|
plt.xlabel("imu_hfdvel_mean (m/s)")
|
|
plt.ylabel("Frequency")
|
|
|
|
pp.savefig()
|
|
plt.close(10)
|
|
|
|
# Output Observer Angular Tracking
|
|
temp = np.asarray([population_data[k].get('output_obs_ang_err_median') for k in found_keys])
|
|
result = 1000.0 * temp[np.isfinite(temp)]
|
|
|
|
if (len(result) > 0):
|
|
population_results['obs_ang_median_avg'][0] = np.mean(result)
|
|
|
|
plt.figure(11,figsize=(20,13))
|
|
|
|
plt.hist(result)
|
|
plt.title("Gaussian Histogram - Output Observer Angular Tracking Error Median")
|
|
plt.xlabel("output_obs_ang_err_median (mrad)")
|
|
plt.ylabel("Frequency")
|
|
|
|
pp.savefig()
|
|
plt.close(11)
|
|
|
|
# Output Observer Velocity Tracking
|
|
temp = np.asarray([population_data[k].get('output_obs_vel_err_median') for k in found_keys])
|
|
result = temp[np.isfinite(temp)]
|
|
|
|
if (len(result) > 0):
|
|
population_results['obs_vel_median_avg'][0] = np.mean(result)
|
|
|
|
plt.figure(12,figsize=(20,13))
|
|
|
|
plt.hist(result)
|
|
plt.title("Gaussian Histogram - Output Observer Velocity Tracking Error Median")
|
|
plt.xlabel("output_obs_ang_err_median (m/s)")
|
|
plt.ylabel("Frequency")
|
|
|
|
pp.savefig()
|
|
plt.close(12)
|
|
|
|
# Output Observer Position Tracking
|
|
temp = np.asarray([population_data[k].get('output_obs_pos_err_median') for k in found_keys])
|
|
result = temp[np.isfinite(temp)]
|
|
|
|
if (len(result) > 0):
|
|
population_results['obs_pos_median_avg'][0] = np.mean(result)
|
|
|
|
plt.figure(13,figsize=(20,13))
|
|
|
|
plt.hist(result)
|
|
plt.title("Gaussian Histogram - Output Observer Position Tracking Error Median")
|
|
plt.xlabel("output_obs_ang_err_median (m)")
|
|
plt.ylabel("Frequency")
|
|
|
|
pp.savefig()
|
|
plt.close(13)
|
|
|
|
# IMU delta angle bias
|
|
temp = np.asarray([population_data[k].get('imu_dang_bias_median') for k in found_keys])
|
|
result = temp[np.isfinite(temp)]
|
|
|
|
if (len(result) > 0):
|
|
plt.figure(14,figsize=(20,13))
|
|
|
|
plt.hist(result)
|
|
plt.title("Gaussian Histogram - IMU Delta Angle Bias Median")
|
|
plt.xlabel("imu_dang_bias_median (rad)")
|
|
plt.ylabel("Frequency")
|
|
|
|
pp.savefig()
|
|
plt.close(14)
|
|
|
|
# IMU delta velocity bias
|
|
temp = np.asarray([population_data[k].get('imu_dvel_bias_median') for k in found_keys])
|
|
result = temp[np.isfinite(temp)]
|
|
|
|
if (len(result) > 0):
|
|
plt.figure(15,figsize=(20,13))
|
|
|
|
plt.hist(result)
|
|
plt.title("Gaussian Histogram - IMU Delta Velocity Bias Median")
|
|
plt.xlabel("imu_dvel_bias_median (m/s)")
|
|
plt.ylabel("Frequency")
|
|
|
|
pp.savefig()
|
|
plt.close(15)
|
|
|
|
# close the pdf file
|
|
pp.close()
|
|
print('Population summary plots saved in population_data.pdf')
|
|
|
|
# don't display to screen
|
|
#plt.show()
|
|
|
|
# clase all figures
|
|
plt.close("all")
|
|
|
|
# write metadata to a .csv file
|
|
population_results_filename = metadata_directory + "/population_data.csv"
|
|
file = open(population_results_filename,"w")
|
|
|
|
file.write("name,value,description\n")
|
|
|
|
# loop through the dictionary and write each entry on a separate row, with data comma separated
|
|
# save data in alphabetical order
|
|
key_list = list(population_results.keys())
|
|
key_list.sort()
|
|
for key in key_list:
|
|
file.write(key+","+str(population_results[key][0])+","+population_results[key][1]+"\n")
|
|
|
|
file.close()
|
|
|
|
print('Population summary data saved in population_data.csv')
|
|
|
|
single_log_results = {
|
|
'filter_faults_max':[float('NaN'),'Largest recorded value of the filter internal fault bitmask. Should always be zero.'],
|
|
'hagl_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the height above ground sensor innovation consistency test.'],
|
|
'hagl_percentage_amber':[float('NaN'),'The percentage of in-flight height above ground sensor innovation consistency test values > 0.5.'],
|
|
'hagl_percentage_red':[float('NaN'),'The percentage of in-flight height above ground sensor innovation consistency test values > 1.0.'],
|
|
'hagl_sensor_status':['Pass','Height above ground sensor check summary. This sensor data is normally sourced from a rangefinder sensor. A Fail result indicates a significant error that caused a significant reduction in vehicle navigation performance was detected. A Warning result indicates that error levels higher than normal were detected but these errors did not significantly impact navigation performance. A Pass result indicates that no amonalies were detected and no further investigation is required'],
|
|
'hagl_test_max':[float('NaN'),'The maximum in-flight value of the height above ground sensor innovation consistency test ratio.'],
|
|
'hagl_test_mean':[float('NaN'),'The mean in-flight value of the height above ground sensor innovation consistency test ratio.'],
|
|
'hgt_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the height sensor innovation consistency test.'],
|
|
'hgt_percentage_amber':[float('NaN'),'The percentage of in-flight height sensor innovation consistency test values > 0.5.'],
|
|
'hgt_percentage_red':[float('NaN'),'The percentage of in-flight height sensor innovation consistency test values > 1.0.'],
|
|
'hgt_sensor_status':['Pass','Height sensor check summary. This sensor data can be sourced from either Baro, GPS, range fidner or external vision system. A Fail result indicates a significant error that caused a significant reduction in vehicle navigation performance was detected. A Warning result indicates that error levels higher than normal were detected but these errors did not significantly impact navigation performance. A Pass result indicates that no amonalies were detected and no further investigation is required'],
|
|
'hgt_test_max':[float('NaN'),'The maximum in-flight value of the height sensor innovation consistency test ratio.'],
|
|
'hgt_test_mean':[float('NaN'),'The mean in-flight value of the height sensor innovation consistency test ratio.'],
|
|
'imu_coning_mean':[float('NaN'),'Mean in-flight value of the IMU delta angle coning vibration metric (rad)'],
|
|
'imu_coning_peak':[float('NaN'),'Peak in-flight value of the IMU delta angle coning vibration metric (rad)'],
|
|
'imu_hfdang_mean':[float('NaN'),'Mean in-flight value of the IMU delta angle high frequency vibration metric (rad)'],
|
|
'imu_hfdang_peak':[float('NaN'),'Peak in-flight value of the IMU delta angle high frequency vibration metric (rad)'],
|
|
'imu_hfdvel_mean':[float('NaN'),'Mean in-flight value of the IMU delta velocity high frequency vibration metric (m/s)'],
|
|
'imu_hfdvel_peak':[float('NaN'),'Peak in-flight value of the IMU delta velocity high frequency vibration metric (m/s)'],
|
|
'imu_sensor_status':['Pass','IMU sensor check summary. A Fail result indicates a significant error that caused a significant reduction in vehicle navigation performance was detected. A Warning result indicates that error levels higher than normal were detected but these errors did not significantly impact navigation performance. A Pass result indicates that no amonalies were detected and no further investigation is required'],
|
|
'in_air_transition_time':[float('NaN'),'The time in seconds measured from startup that the EKF transtioned into in-air mode. Set to a nan if a transition event is not detected.'],
|
|
'mag_percentage_amber':[float('NaN'),'The percentage of in-flight consolidated magnetic field sensor innovation consistency test values > 0.5.'],
|
|
'mag_percentage_red':[float('NaN'),'The percentage of in-flight consolidated magnetic field sensor innovation consistency test values > 1.0.'],
|
|
'mag_sensor_status':['Pass','Magnetometer sensor check summary. A Fail result indicates a significant error that caused a significant reduction in vehicle navigation performance was detected. A Warning result indicates that error levels higher than normal were detected but these errors did not significantly impact navigation performance. A Pass result indicates that no amonalies were detected and no further investigation is required'],
|
|
'mag_test_max':[float('NaN'),'The maximum in-flight value of the magnetic field sensor innovation consistency test ratio.'],
|
|
'mag_test_mean':[float('NaN'),'The mean in-flight value of the magnetic field sensor innovation consistency test ratio.'],
|
|
'magx_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the X-axis magnetic field sensor innovation consistency test.'],
|
|
'magy_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the Y-axis magnetic field sensor innovation consistency test.'],
|
|
'magz_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the Z-axis magnetic field sensor innovation consistency test.'],
|
|
'master_status':['Pass','Master check status which can be either Pass Warning or Fail. A Fail result indicates a significant error that caused a significant reduction in vehicle navigation performance was detected. A Warning result indicates that error levels higher than normal were detected but these errors did not significantly impact navigation performance. A Pass result indicates that no amonalies were detected and no further investigation is required'],
|
|
'ofx_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the optical flow sensor X-axis innovation consistency test.'],
|
|
'ofy_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the optical flow sensor Y-axis innovation consistency test.'],
|
|
'on_ground_transition_time':[float('NaN'),'The time in seconds measured from startup that the EKF transitioned out of in-air mode. Set to a nan if a transition event is not detected.'],
|
|
'output_obs_ang_err_median':[float('NaN'),'Median in-flight value of the output observer angular error (rad)'],
|
|
'output_obs_pos_err_median':[float('NaN'),'Median in-flight value of the output observer position error (m)'],
|
|
'output_obs_vel_err_median':[float('NaN'),'Median in-flight value of the output observer velocity error (m/s)'],
|
|
'pos_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the velocity sensor consolidated innovation consistency test.'],
|
|
'pos_percentage_amber':[float('NaN'),'The percentage of in-flight position sensor consolidated innovation consistency test values > 0.5.'],
|
|
'pos_percentage_red':[float('NaN'),'The percentage of in-flight position sensor consolidated innovation consistency test values > 1.0.'],
|
|
'pos_sensor_status':['Pass','Position sensor check summary. A Fail result indicates a significant error that caused a significant reduction in vehicle navigation performance was detected. A Warning result indicates that error levels higher than normal were detected but these errors did not significantly impact navigation performance. A Pass result indicates that no amonalies were detected and no further investigation is required'],
|
|
'pos_test_max':[float('NaN'),'The maximum in-flight value of the position sensor consolidated innovation consistency test ratio.'],
|
|
'pos_test_mean':[float('NaN'),'The mean in-flight value of the position sensor consolidated innovation consistency test ratio.'],
|
|
'tas_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the airspeed sensor innovation consistency test.'],
|
|
'tas_percentage_amber':[float('NaN'),'The percentage of in-flight airspeed sensor innovation consistency test values > 0.5.'],
|
|
'tas_percentage_red':[float('NaN'),'The percentage of in-flight airspeed sensor innovation consistency test values > 1.0.'],
|
|
'tas_sensor_status':['Pass','Airspeed sensor check summary. A Fail result indicates a significant error that caused a significant reduction in vehicle navigation performance was detected. A Warning result indicates that error levels higher than normal were detected but these errors did not significantly impact navigation performance. A Pass result indicates that no amonalies were detected and no further investigation is required'],
|
|
'tas_test_max':[float('NaN'),'The maximum in-flight value of the airspeed sensor innovation consistency test ratio.'],
|
|
'tas_test_mean':[float('NaN'),'The mean in-flight value of the airspeed sensor innovation consistency test ratio.'],
|
|
'tilt_align_time':[float('NaN'),'The time in seconds measured from startup that the EKF completed the tilt alignment. A nan value indicates that the alignment had completed before logging started or alignment did not complete.'],
|
|
'vel_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the velocity sensor consolidated innovation consistency test.'],
|
|
'vel_percentage_amber':[float('NaN'),'The percentage of in-flight velocity sensor consolidated innovation consistency test values > 0.5.'],
|
|
'vel_percentage_red':[float('NaN'),'The percentage of in-flight velocity sensor consolidated innovation consistency test values > 1.0.'],
|
|
'vel_sensor_status':['Pass','Velocity sensor check summary. A Fail result indicates a significant error that caused a significant reduction in vehicle navigation performance was detected. A Warning result indicates that error levels higher than normal were detected but these errors did not significantly impact navigation performance. A Pass result indicates that no amonalies were detected and no further investigation is required'],
|
|
'vel_test_max':[float('NaN'),'The maximum in-flight value of the velocity sensor consolidated innovation consistency test ratio.'],
|
|
'vel_test_mean':[float('NaN'),'The mean in-flight value of the velocity sensor consolidated innovation consistency test ratio.'],
|
|
'yaw_align_time':[float('NaN'),'The time in seconds measured from startup that the EKF completed the yaw alignment.'],
|
|
'yaw_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the yaw sensor innovation consistency test.'],
|
|
'yaw_sensor_status':['Pass','Yaw sensor check summary. This sensor data can be sourced from the magnetometer or an external vision system. A Fail result indicates a significant error that caused a significant reduction in vehicle navigation performance was detected. A Warning result indicates that error levels higher than normal were detected but these errors did not significantly impact navigation performance. A Pass result indicates that no amonalies were detected and no further investigation is required'],
|
|
}
|