forked from Archive/PX4-Autopilot
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])
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result1 = temp[np.isfinite(temp)]
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temp = np.asarray([population_data[k].get('imu_hfdvel_mean') for k in found_keys])
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|
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'],
|
|
}
|