def analyse_ekf(
ulog: ULog, check_levels: Dict[str, float], red_thresh: float = 1.0,
amb_thresh: float = 0.5, min_flight_duration_seconds: float = 5.0,
in_air_margin_seconds: float = 5.0, pos_checks_when_sensors_not_fused: bool = False) -> \
Tuple[str, Dict[str, str], Dict[str, float], Dict[str, float]]:
"""
:param ulog:
:param check_levels:
:param red_thresh:
:param amb_thresh:
:param min_flight_duration_seconds:
:param in_air_margin_seconds:
:param pos_checks_when_sensors_not_fused:
:return:
"""
try:
estimator_status = ulog.get_dataset('estimator_status').data
print('found estimator_status data')
except:
raise PreconditionError('could not find estimator_status data')
try:
_ = ulog.get_dataset('ekf2_innovations').data
print('found ekf2_innovation data')
except:
raise PreconditionError('could not find ekf2_innovation data')
try:
in_air = InAirDetector(
ulog, min_flight_time_seconds=min_flight_duration_seconds, in_air_margin_seconds=0.0)
in_air_no_ground_effects = InAirDetector(
ulog, min_flight_time_seconds=min_flight_duration_seconds,
in_air_margin_seconds=in_air_margin_seconds)
except Exception as e:
raise PreconditionError(str(e))
if in_air_no_ground_effects.take_off is None:
raise PreconditionError('no airtime detected.')
airtime_info = {
'in_air_transition_time': round(in_air.take_off + in_air.log_start, 2),
'on_ground_transition_time': round(in_air.landing + in_air.log_start, 2)}
control_mode, innov_flags, gps_fail_flags = get_estimator_check_flags(estimator_status)
sensor_checks, innov_fail_checks = find_checks_that_apply(
control_mode, estimator_status,
pos_checks_when_sensors_not_fused=pos_checks_when_sensors_not_fused)
metrics = calculate_ecl_ekf_metrics(
ulog, innov_flags, innov_fail_checks, sensor_checks, in_air, in_air_no_ground_effects,
red_thresh=red_thresh, amb_thresh=amb_thresh)
check_status, master_status = perform_ecl_ekf_checks(
metrics, sensor_checks, innov_fail_checks, check_levels)
return master_status, check_status, metrics, airtime_info
def analyse_ekf(
ulog: ULog, check_levels: Dict[str, float], red_thresh: float = 1.0,
amb_thresh: float = 0.5, min_flight_duration_seconds: float = 5.0,
in_air_margin_seconds: float = 5.0, pos_checks_when_sensors_not_fused: bool = False) -> \
Tuple[str, Dict[str, str], Dict[str, float], Dict[str, float]]:
"""
:param ulog:
:param check_levels:
:param red_thresh:
:param amb_thresh:
:param min_flight_duration_seconds:
:param in_air_margin_seconds:
:param pos_checks_when_sensors_not_fused:
:return:
"""
try:
estimator_states = ulog.get_dataset('estimator_states').data
print('found estimator_states data')
except:
raise PreconditionError('could not find estimator_states data')
try:
estimator_status = ulog.get_dataset('estimator_status').data
print('found estimator_status data')
except:
raise PreconditionError('could not find estimator_status data')
try:
_ = ulog.get_dataset('estimator_innovations').data
print('found estimator_innovations data')
except:
raise PreconditionError('could not find estimator_innovations data')
try:
in_air = InAirDetector(
ulog, min_flight_time_seconds=min_flight_duration_seconds, in_air_margin_seconds=0.0)
in_air_no_ground_effects = InAirDetector(
ulog, min_flight_time_seconds=min_flight_duration_seconds,
in_air_margin_seconds=in_air_margin_seconds)
except Exception as e:
raise PreconditionError(str(e))
if in_air_no_ground_effects.take_off is None:
raise PreconditionError('no airtime detected.')
airtime_info = {
'in_air_transition_time': round(in_air.take_off + in_air.log_start, 2),
'on_ground_transition_time': round(in_air.landing + in_air.log_start, 2)}
control_mode, innov_flags, gps_fail_flags = get_estimator_check_flags(estimator_status)
sensor_checks, innov_fail_checks = find_checks_that_apply(
control_mode, estimator_status,
pos_checks_when_sensors_not_fused=pos_checks_when_sensors_not_fused)
metrics = calculate_ecl_ekf_metrics(
ulog, innov_flags, innov_fail_checks, sensor_checks, in_air, in_air_no_ground_effects,
red_thresh=red_thresh, amb_thresh=amb_thresh)
check_status, master_status = perform_ecl_ekf_checks(
metrics, sensor_checks, innov_fail_checks, check_levels)
return master_status, check_status, metrics, airtime_info
def create_pdf_report(ulog: ULog, output_plot_filename: str) -> None:
"""
creates a pdf report of the ekf analysis.
:param ulog:
:param output_plot_filename:
:return:
"""
# create summary plots
# save the plots to PDF
try:
estimator_status = ulog.get_dataset('estimator_status').data
print('found estimator_status data')
except:
raise PreconditionError('could not find estimator_status data')
try:
estimator_innovations = ulog.get_dataset('estimator_innovations').data
estimator_innovation_variances = ulog.get_dataset(
'estimator_innovation_variances').data
innovation_data = estimator_innovations
for key in estimator_innovation_variances:
# append 'var' to the field name such that we can distingush between innov and innov_var
innovation_data.update(
{str('var_' + key): estimator_innovation_variances[key]})
print('found innovation data')
except:
raise PreconditionError('could not find innovation data')
try:
sensor_preflight = ulog.get_dataset('sensor_preflight').data
print('found sensor_preflight data')
except:
raise PreconditionError('could not find sensor_preflight data')
control_mode, innov_flags, gps_fail_flags = get_estimator_check_flags(
estimator_status)
status_time = 1e-6 * estimator_status['timestamp']
b_finishes_in_air, b_starts_in_air, in_air_duration, in_air_transition_time, \
on_ground_transition_time = detect_airtime(control_mode, status_time)
with PdfPages(output_plot_filename) as pdf_pages:
# plot IMU consistency data
if ('accel_inconsistency_m_s_s'
in sensor_preflight.keys()) and ('gyro_inconsistency_rad_s'
in sensor_preflight.keys()):
data_plot = TimeSeriesPlot(
sensor_preflight,
[['accel_inconsistency_m_s_s'], ['gyro_inconsistency_rad_s']],
x_labels=['data index', 'data index'],
y_labels=['acceleration (m/s/s)', 'angular rate (rad/s)'],
plot_title='IMU Consistency Check Levels',
pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# vertical velocity and position innovations
data_plot = InnovationPlot(innovation_data,
[('gps_vpos', 'var_gps_vpos'),
('gps_vvel', 'var_gps_vvel')],
x_labels=['time (sec)', 'time (sec)'],
y_labels=['Down Vel (m/s)', 'Down Pos (m)'],
plot_title='Vertical Innovations',
pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# horizontal velocity innovations
data_plot = InnovationPlot(
innovation_data, [('gps_hvel[0]', 'var_gps_hvel[0]'),
('gps_hvel[1]', 'var_gps_hvel[1]')],
x_labels=['time (sec)', 'time (sec)'],
y_labels=['North Vel (m/s)', 'East Vel (m/s)'],
plot_title='Horizontal Velocity Innovations',
pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# horizontal position innovations
data_plot = InnovationPlot(
innovation_data, [('gps_hpos[0]', 'var_gps_hpos[0]'),
('gps_hpos[1]', 'var_gps_hpos[1]')],
x_labels=['time (sec)', 'time (sec)'],
y_labels=['North Pos (m)', 'East Pos (m)'],
plot_title='Horizontal Position Innovations',
pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# magnetometer innovations
data_plot = InnovationPlot(
innovation_data, [('mag_field[0]', 'var_mag_field[0]'),
('mag_field[1]', 'var_mag_field[1]'),
('mag_field[2]', 'var_mag_field[2]')],
x_labels=['time (sec)', 'time (sec)', 'time (sec)'],
y_labels=['X (Gauss)', 'Y (Gauss)', 'Z (Gauss)'],
plot_title='Magnetometer Innovations',
pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# magnetic heading innovations
data_plot = InnovationPlot(innovation_data,
[('heading', 'var_heading')],
x_labels=['time (sec)'],
y_labels=['Heading (rad)'],
plot_title='Magnetic Heading Innovations',
pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# air data innovations
data_plot = InnovationPlot(
innovation_data, [('airspeed', 'var_airspeed'),
('beta', 'var_beta')],
x_labels=['time (sec)', 'time (sec)'],
y_labels=['innovation (m/sec)', 'innovation (rad)'],
sub_titles=[
'True Airspeed Innovations', 'Synthetic Sideslip Innovations'
],
pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# optical flow innovations
data_plot = InnovationPlot(innovation_data,
[('flow[0]', 'var_flow[0]'),
('flow[1]', 'var_flow[1]')],
x_labels=['time (sec)', 'time (sec)'],
y_labels=['X (rad/sec)', 'Y (rad/sec)'],
plot_title='Optical Flow Innovations',
pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# plot normalised innovation test levels
# define variables to plot
variables = [['mag_test_ratio'], ['vel_test_ratio', 'pos_test_ratio'],
['hgt_test_ratio']]
y_labels = ['mag', 'vel, pos', 'hgt']
legend = [['mag'], ['vel', 'pos'], ['hgt']]
if np.amax(estimator_status['hagl_test_ratio']
) > 0.0: # plot hagl test ratio, if applicable
variables[-1].append('hagl_test_ratio')
y_labels[-1] += ', hagl'
legend[-1].append('hagl')
if np.amax(estimator_status['tas_test_ratio']
) > 0.0: # plot airspeed sensor test ratio, if applicable
variables.append(['tas_test_ratio'])
y_labels.append('TAS')
legend.append(['airspeed'])
data_plot = CheckFlagsPlot(
status_time,
estimator_status,
variables,
x_label='time (sec)',
y_labels=y_labels,
plot_title='Normalised Innovation Test Levels',
pdf_handle=pdf_pages,
annotate=True,
legend=legend)
data_plot.save()
data_plot.close()
# plot control mode summary A
data_plot = ControlModeSummaryPlot(
status_time,
control_mode,
[['tilt_aligned', 'yaw_aligned'],
['using_gps', 'using_optflow', 'using_evpos'],
['using_barohgt', 'using_gpshgt', 'using_rnghgt', 'using_evhgt'],
['using_magyaw', 'using_mag3d', 'using_magdecl']],
x_label='time (sec)',
y_labels=['aligned', 'pos aiding', 'hgt aiding', 'mag aiding'],
annotation_text=[['tilt alignment', 'yaw alignment'],
[
'GPS aiding', 'optical flow aiding',
'external vision aiding'
],
[
'Baro aiding', 'GPS aiding',
'rangefinder aiding', 'external vision aiding'
],
[
'magnetic yaw aiding',
'3D magnetoemter aiding',
'magnetic declination aiding'
]],
plot_title='EKF Control Status - Figure A',
pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# plot control mode summary B
# construct additional annotations for the airborne plot
airborne_annotations = list()
if np.amin(np.diff(control_mode['airborne'])) > -0.5:
airborne_annotations.append(
(on_ground_transition_time,
'air to ground transition not detected'))
else:
airborne_annotations.append(
(on_ground_transition_time,
'on-ground at {:.1f} sec'.format(on_ground_transition_time)))
if in_air_duration > 0.0:
airborne_annotations.append(
((in_air_transition_time + on_ground_transition_time) / 2,
'duration = {:.1f} sec'.format(in_air_duration)))
if np.amax(np.diff(control_mode['airborne'])) < 0.5:
airborne_annotations.append(
(in_air_transition_time,
'ground to air transition not detected'))
else:
airborne_annotations.append(
(in_air_transition_time,
'in-air at {:.1f} sec'.format(in_air_transition_time)))
data_plot = ControlModeSummaryPlot(
status_time,
control_mode, [['airborne'], ['estimating_wind']],
x_label='time (sec)',
y_labels=['airborne', 'estimating wind'],
annotation_text=[[], []],
additional_annotation=[airborne_annotations, []],
plot_title='EKF Control Status - Figure B',
pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# plot innovation_check_flags summary
data_plot = CheckFlagsPlot(
status_time,
innov_flags, [['vel_innov_fail', 'posh_innov_fail'],
['posv_innov_fail', 'hagl_innov_fail'],
[
'magx_innov_fail', 'magy_innov_fail',
'magz_innov_fail', 'yaw_innov_fail'
], ['tas_innov_fail'], ['sli_innov_fail'],
['ofx_innov_fail', 'ofy_innov_fail']],
x_label='time (sec)',
y_labels=[
'failed', 'failed', 'failed', 'failed', 'failed', 'failed'
],
y_lim=(-0.1, 1.1),
legend=[['vel NED', 'pos NE'],
['hgt absolute', 'hgt above ground'],
['mag_x', 'mag_y', 'mag_z', 'yaw'], ['airspeed'],
['sideslip'], ['flow X', 'flow Y']],
plot_title='EKF Innovation Test Fails',
annotate=False,
pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# gps_check_fail_flags summary
data_plot = CheckFlagsPlot(
status_time,
gps_fail_flags, [[
'nsat_fail', 'pdop_fail', 'herr_fail', 'verr_fail',
'gfix_fail', 'serr_fail'
], ['hdrift_fail', 'vdrift_fail', 'hspd_fail', 'veld_diff_fail']],
x_label='time (sec)',
y_lim=(-0.1, 1.1),
y_labels=['failed', 'failed'],
sub_titles=[
'GPS Direct Output Check Failures',
'GPS Derived Output Check Failures'
],
legend=[[
'N sats', 'PDOP', 'horiz pos error', 'vert pos error',
'fix type', 'speed error'
],
[
'horiz drift', 'vert drift', 'horiz speed',
'vert vel inconsistent'
]],
annotate=False,
pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# filter reported accuracy
data_plot = CheckFlagsPlot(
status_time,
estimator_status, [['pos_horiz_accuracy', 'pos_vert_accuracy']],
x_label='time (sec)',
y_labels=['accuracy (m)'],
plot_title='Reported Accuracy',
legend=[['horizontal', 'vertical']],
annotate=False,
pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# Plot the EKF IMU vibration metrics
scaled_estimator_status = {
'vibe[0]': 1000. * estimator_status['vibe[0]'],
'vibe[1]': 1000. * estimator_status['vibe[1]'],
'vibe[2]': estimator_status['vibe[2]']
}
data_plot = CheckFlagsPlot(status_time,
scaled_estimator_status,
[['vibe[0]'], ['vibe[1]'], ['vibe[2]']],
x_label='time (sec)',
y_labels=[
'Del Ang Coning (mrad)',
'HF Del Ang (mrad)', 'HF Del Vel (m/s)'
],
plot_title='IMU Vibration Metrics',
pdf_handle=pdf_pages,
annotate=True)
data_plot.save()
data_plot.close()
# Plot the EKF output observer tracking errors
scaled_innovations = {
'output_tracking_error[0]':
1000. * estimator_status['output_tracking_error[0]'],
'output_tracking_error[1]':
estimator_status['output_tracking_error[1]'],
'output_tracking_error[2]':
estimator_status['output_tracking_error[2]']
}
data_plot = CheckFlagsPlot(
1e-6 * estimator_status['timestamp'],
scaled_innovations,
[['output_tracking_error[0]'], ['output_tracking_error[1]'],
['output_tracking_error[2]']],
x_label='time (sec)',
y_labels=['angles (mrad)', 'velocity (m/s)', 'position (m)'],
plot_title='Output Observer Tracking Error Magnitudes',
pdf_handle=pdf_pages,
annotate=True)
data_plot.save()
data_plot.close()
# Plot the delta angle bias estimates
data_plot = CheckFlagsPlot(
1e-6 * estimator_status['timestamp'],
estimator_status, [['states[10]'], ['states[11]'], ['states[12]']],
x_label='time (sec)',
y_labels=['X (rad)', 'Y (rad)', 'Z (rad)'],
plot_title='Delta Angle Bias Estimates',
annotate=False,
pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# Plot the delta velocity bias estimates
data_plot = CheckFlagsPlot(
1e-6 * estimator_status['timestamp'],
estimator_status, [['states[13]'], ['states[14]'], ['states[15]']],
x_label='time (sec)',
y_labels=['X (m/s)', 'Y (m/s)', 'Z (m/s)'],
plot_title='Delta Velocity Bias Estimates',
annotate=False,
pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# Plot the earth frame magnetic field estimates
declination, field_strength, inclination = magnetic_field_estimates_from_status(
estimator_status)
data_plot = CheckFlagsPlot(1e-6 * estimator_status['timestamp'], {
'strength': field_strength,
'declination': declination,
'inclination': inclination
}, [['declination'], ['inclination'], ['strength']],
x_label='time (sec)',
y_labels=[
'declination (deg)',
'inclination (deg)', 'strength (Gauss)'
],
plot_title='Earth Magnetic Field Estimates',
annotate=False,
pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# Plot the body frame magnetic field estimates
data_plot = CheckFlagsPlot(
1e-6 * estimator_status['timestamp'],
estimator_status, [['states[19]'], ['states[20]'], ['states[21]']],
x_label='time (sec)',
y_labels=['X (Gauss)', 'Y (Gauss)', 'Z (Gauss)'],
plot_title='Magnetometer Bias Estimates',
annotate=False,
pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# Plot the EKF wind estimates
data_plot = CheckFlagsPlot(1e-6 * estimator_status['timestamp'],
estimator_status,
[['states[22]'], ['states[23]']],
x_label='time (sec)',
y_labels=['North (m/s)', 'East (m/s)'],
plot_title='Wind Velocity Estimates',
annotate=False,
pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
def create_pdf_report(ulog: ULog, output_plot_filename: str) -> None:
"""
creates a pdf report of the ekf analysis.
:param ulog:
:param output_plot_filename:
:return:
"""
# create summary plots
# save the plots to PDF
try:
estimator_status = ulog.get_dataset('estimator_status').data
print('found estimator_status data')
except:
raise PreconditionError('could not find estimator_status data')
try:
ekf2_innovations = ulog.get_dataset('ekf2_innovations').data
print('found ekf2_innovation data')
except:
raise PreconditionError('could not find ekf2_innovation data')
try:
sensor_preflight = ulog.get_dataset('sensor_preflight').data
print('found sensor_preflight data')
except:
raise PreconditionError('could not find sensor_preflight data')
control_mode, innov_flags, gps_fail_flags = get_estimator_check_flags(estimator_status)
status_time = 1e-6 * estimator_status['timestamp']
b_finishes_in_air, b_starts_in_air, in_air_duration, in_air_transition_time, \
on_ground_transition_time = detect_airtime(control_mode, status_time)
with PdfPages(output_plot_filename) as pdf_pages:
# plot IMU consistency data
if ('accel_inconsistency_m_s_s' in sensor_preflight.keys()) and (
'gyro_inconsistency_rad_s' in sensor_preflight.keys()):
data_plot = TimeSeriesPlot(
sensor_preflight, [['accel_inconsistency_m_s_s'], ['gyro_inconsistency_rad_s']],
x_labels=['data index', 'data index'],
y_labels=['acceleration (m/s/s)', 'angular rate (rad/s)'],
plot_title='IMU Consistency Check Levels', pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# vertical velocity and position innovations
data_plot = InnovationPlot(
ekf2_innovations, [('vel_pos_innov[2]', 'vel_pos_innov_var[2]'),
('vel_pos_innov[5]', 'vel_pos_innov_var[5]')],
x_labels=['time (sec)', 'time (sec)'],
y_labels=['Down Vel (m/s)', 'Down Pos (m)'], plot_title='Vertical Innovations',
pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# horizontal velocity innovations
data_plot = InnovationPlot(
ekf2_innovations, [('vel_pos_innov[0]', 'vel_pos_innov_var[0]'),
('vel_pos_innov[1]','vel_pos_innov_var[1]')],
x_labels=['time (sec)', 'time (sec)'],
y_labels=['North Vel (m/s)', 'East Vel (m/s)'],
plot_title='Horizontal Velocity Innovations', pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# horizontal position innovations
data_plot = InnovationPlot(
ekf2_innovations, [('vel_pos_innov[3]', 'vel_pos_innov_var[3]'), ('vel_pos_innov[4]',
'vel_pos_innov_var[4]')],
x_labels=['time (sec)', 'time (sec)'],
y_labels=['North Pos (m)', 'East Pos (m)'], plot_title='Horizontal Position Innovations',
pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# magnetometer innovations
data_plot = InnovationPlot(
ekf2_innovations, [('mag_innov[0]', 'mag_innov_var[0]'),
('mag_innov[1]', 'mag_innov_var[1]'), ('mag_innov[2]', 'mag_innov_var[2]')],
x_labels=['time (sec)', 'time (sec)', 'time (sec)'],
y_labels=['X (Gauss)', 'Y (Gauss)', 'Z (Gauss)'], plot_title='Magnetometer Innovations',
pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# magnetic heading innovations
data_plot = InnovationPlot(
ekf2_innovations, [('heading_innov', 'heading_innov_var')],
x_labels=['time (sec)'], y_labels=['Heading (rad)'],
plot_title='Magnetic Heading Innovations', pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# air data innovations
data_plot = InnovationPlot(
ekf2_innovations,
[('airspeed_innov', 'airspeed_innov_var'), ('beta_innov', 'beta_innov_var')],
x_labels=['time (sec)', 'time (sec)'],
y_labels=['innovation (m/sec)', 'innovation (rad)'],
sub_titles=['True Airspeed Innovations', 'Synthetic Sideslip Innovations'],
pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# optical flow innovations
data_plot = InnovationPlot(
ekf2_innovations, [('flow_innov[0]', 'flow_innov_var[0]'), ('flow_innov[1]',
'flow_innov_var[1]')],
x_labels=['time (sec)', 'time (sec)'],
y_labels=['X (rad/sec)', 'Y (rad/sec)'],
plot_title='Optical Flow Innovations', pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# plot normalised innovation test levels
# define variables to plot
variables = [['mag_test_ratio'], ['vel_test_ratio', 'pos_test_ratio'], ['hgt_test_ratio']]
y_labels = ['mag', 'vel, pos', 'hgt']
legend = [['mag'], ['vel', 'pos'], ['hgt']]
if np.amax(estimator_status['hagl_test_ratio']) > 0.0: # plot hagl test ratio, if applicable
variables[-1].append('hagl_test_ratio')
y_labels[-1] += ', hagl'
legend[-1].append('hagl')
if np.amax(estimator_status[
'tas_test_ratio']) > 0.0: # plot airspeed sensor test ratio, if applicable
variables.append(['tas_test_ratio'])
y_labels.append('TAS')
legend.append(['airspeed'])
data_plot = CheckFlagsPlot(
status_time, estimator_status, variables, x_label='time (sec)', y_labels=y_labels,
plot_title='Normalised Innovation Test Levels', pdf_handle=pdf_pages, annotate=True,
legend=legend
)
data_plot.save()
data_plot.close()
# plot control mode summary A
data_plot = ControlModeSummaryPlot(
status_time, control_mode, [['tilt_aligned', 'yaw_aligned'],
['using_gps', 'using_optflow', 'using_evpos'], ['using_barohgt', 'using_gpshgt',
'using_rnghgt', 'using_evhgt'], ['using_magyaw', 'using_mag3d', 'using_magdecl']],
x_label='time (sec)', y_labels=['aligned', 'pos aiding', 'hgt aiding', 'mag aiding'],
annotation_text=[['tilt alignment', 'yaw alignment'], ['GPS aiding', 'optical flow aiding',
'external vision aiding'], ['Baro aiding', 'GPS aiding', 'rangefinder aiding',
'external vision aiding'], ['magnetic yaw aiding', '3D magnetoemter aiding',
'magnetic declination aiding']], plot_title='EKF Control Status - Figure A',
pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# plot control mode summary B
# construct additional annotations for the airborne plot
airborne_annotations = list()
if np.amin(np.diff(control_mode['airborne'])) > -0.5:
airborne_annotations.append(
(on_ground_transition_time, 'air to ground transition not detected'))
else:
airborne_annotations.append((on_ground_transition_time, 'on-ground at {:.1f} sec'.format(
on_ground_transition_time)))
if in_air_duration > 0.0:
airborne_annotations.append(((in_air_transition_time + on_ground_transition_time) / 2,
'duration = {:.1f} sec'.format(in_air_duration)))
if np.amax(np.diff(control_mode['airborne'])) < 0.5:
airborne_annotations.append(
(in_air_transition_time, 'ground to air transition not detected'))
else:
airborne_annotations.append(
(in_air_transition_time, 'in-air at {:.1f} sec'.format(in_air_transition_time)))
data_plot = ControlModeSummaryPlot(
status_time, control_mode, [['airborne'], ['estimating_wind']],
x_label='time (sec)', y_labels=['airborne', 'estimating wind'], annotation_text=[[], []],
additional_annotation=[airborne_annotations, []],
plot_title='EKF Control Status - Figure B', pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# plot innovation_check_flags summary
data_plot = CheckFlagsPlot(
status_time, innov_flags, [['vel_innov_fail', 'posh_innov_fail'], ['posv_innov_fail',
'hagl_innov_fail'],
['magx_innov_fail', 'magy_innov_fail', 'magz_innov_fail',
'yaw_innov_fail'], ['tas_innov_fail'], ['sli_innov_fail'],
['ofx_innov_fail',
'ofy_innov_fail']], x_label='time (sec)',
y_labels=['failed', 'failed', 'failed', 'failed', 'failed', 'failed'],
y_lim=(-0.1, 1.1),
legend=[['vel NED', 'pos NE'], ['hgt absolute', 'hgt above ground'],
['mag_x', 'mag_y', 'mag_z', 'yaw'], ['airspeed'], ['sideslip'],
['flow X', 'flow Y']],
plot_title='EKF Innovation Test Fails', annotate=False, pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# gps_check_fail_flags summary
data_plot = CheckFlagsPlot(
status_time, gps_fail_flags,
[['nsat_fail', 'gdop_fail', 'herr_fail', 'verr_fail', 'gfix_fail', 'serr_fail'],
['hdrift_fail', 'vdrift_fail', 'hspd_fail', 'veld_diff_fail']],
x_label='time (sec)', y_lim=(-0.1, 1.1), y_labels=['failed', 'failed'],
sub_titles=['GPS Direct Output Check Failures', 'GPS Derived Output Check Failures'],
legend=[['N sats', 'GDOP', 'horiz pos error', 'vert pos error', 'fix type',
'speed error'], ['horiz drift', 'vert drift', 'horiz speed',
'vert vel inconsistent']], annotate=False, pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# filter reported accuracy
data_plot = CheckFlagsPlot(
status_time, estimator_status, [['pos_horiz_accuracy', 'pos_vert_accuracy']],
x_label='time (sec)', y_labels=['accuracy (m)'], plot_title='Reported Accuracy',
legend=[['horizontal', 'vertical']], annotate=False, pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# Plot the EKF IMU vibration metrics
scaled_estimator_status = {'vibe[0]': 1000. * estimator_status['vibe[0]'],
'vibe[1]': 1000. * estimator_status['vibe[1]'],
'vibe[2]': estimator_status['vibe[2]']
}
data_plot = CheckFlagsPlot(
status_time, scaled_estimator_status, [['vibe[0]'], ['vibe[1]'], ['vibe[2]']],
x_label='time (sec)', y_labels=['Del Ang Coning (mrad)', 'HF Del Ang (mrad)',
'HF Del Vel (m/s)'], plot_title='IMU Vibration Metrics',
pdf_handle=pdf_pages, annotate=True)
data_plot.save()
data_plot.close()
# Plot the EKF output observer tracking errors
scaled_innovations = {
'output_tracking_error[0]': 1000. * ekf2_innovations['output_tracking_error[0]'],
'output_tracking_error[1]': ekf2_innovations['output_tracking_error[1]'],
'output_tracking_error[2]': ekf2_innovations['output_tracking_error[2]']
}
data_plot = CheckFlagsPlot(
1e-6 * ekf2_innovations['timestamp'], scaled_innovations,
[['output_tracking_error[0]'], ['output_tracking_error[1]'],
['output_tracking_error[2]']], x_label='time (sec)',
y_labels=['angles (mrad)', 'velocity (m/s)', 'position (m)'],
plot_title='Output Observer Tracking Error Magnitudes',
pdf_handle=pdf_pages, annotate=True)
data_plot.save()
data_plot.close()
# Plot the delta angle bias estimates
data_plot = CheckFlagsPlot(
1e-6 * estimator_status['timestamp'], estimator_status,
[['states[10]'], ['states[11]'], ['states[12]']],
x_label='time (sec)', y_labels=['X (rad)', 'Y (rad)', 'Z (rad)'],
plot_title='Delta Angle Bias Estimates', annotate=False, pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# Plot the delta velocity bias estimates
data_plot = CheckFlagsPlot(
1e-6 * estimator_status['timestamp'], estimator_status,
[['states[13]'], ['states[14]'], ['states[15]']],
x_label='time (sec)', y_labels=['X (m/s)', 'Y (m/s)', 'Z (m/s)'],
plot_title='Delta Velocity Bias Estimates', annotate=False, pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# Plot the earth frame magnetic field estimates
declination, field_strength, inclination = magnetic_field_estimates_from_status(
estimator_status)
data_plot = CheckFlagsPlot(
1e-6 * estimator_status['timestamp'],
{'strength': field_strength, 'declination': declination, 'inclination': inclination},
[['declination'], ['inclination'], ['strength']],
x_label='time (sec)', y_labels=['declination (deg)', 'inclination (deg)',
'strength (Gauss)'],
plot_title='Earth Magnetic Field Estimates', annotate=False,
pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# Plot the body frame magnetic field estimates
data_plot = CheckFlagsPlot(
1e-6 * estimator_status['timestamp'], estimator_status,
[['states[19]'], ['states[20]'], ['states[21]']],
x_label='time (sec)', y_labels=['X (Gauss)', 'Y (Gauss)', 'Z (Gauss)'],
plot_title='Magnetometer Bias Estimates', annotate=False, pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()
# Plot the EKF wind estimates
data_plot = CheckFlagsPlot(
1e-6 * estimator_status['timestamp'], estimator_status,
[['states[22]'], ['states[23]']], x_label='time (sec)',
y_labels=['North (m/s)', 'East (m/s)'], plot_title='Wind Velocity Estimates',
annotate=False, pdf_handle=pdf_pages)
data_plot.save()
data_plot.close()