ekf2_post-processing: use estimator_status_flags instead of bitmasks

Tools/ecl_ekf/analyse_logdata_ekf.py

@@ -11,7 +11,7 @@ from pyulog import ULog
 from analysis.detectors import InAirDetector, PreconditionError
 from analysis.metrics import calculate_ecl_ekf_metrics
 from analysis.checks import perform_ecl_ekf_checks
-from analysis.post_processing import get_estimator_check_flags
+from analysis.post_processing import get_gps_check_fail_flags
 
 def analyse_ekf(
         ulog: ULog, check_levels: Dict[str, float], multi_instance: int = 0,
@@ -40,6 +40,11 @@ def analyse_ekf(
     except:
         raise PreconditionError('could not find estimator_status instance', multi_instance)
 
+    try:
+        estimator_status_flags = ulog.get_dataset('estimator_status_flags', multi_instance).data
+    except:
+        raise PreconditionError('could not find estimator_status_flags instance', multi_instance)
+
     try:
         _ = ulog.get_dataset('estimator_innovations', multi_instance).data
     except:
@@ -61,14 +66,14 @@ def analyse_ekf(
         '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)
+    gps_fail_flags = get_gps_check_fail_flags(estimator_status)
 
     sensor_checks, innov_fail_checks = find_checks_that_apply(
-        control_mode, estimator_status,
+        estimator_status_flags, 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,
+        ulog, estimator_status_flags, innov_fail_checks, sensor_checks, in_air, in_air_no_ground_effects,
         multi_instance, red_thresh=red_thresh, amb_thresh=amb_thresh)
 
     check_status, master_status = perform_ecl_ekf_checks(
@@ -78,12 +83,12 @@ def analyse_ekf(
 
 
 def find_checks_that_apply(
-    control_mode: dict, estimator_status: dict, pos_checks_when_sensors_not_fused: bool = False) ->\
+    estimator_status_flags: dict, estimator_status: dict, pos_checks_when_sensors_not_fused: bool = False) ->\
         Tuple[List[str], List[str]]:
     """
     finds the checks that apply and stores them in lists for the std checks and the innovation
     fail checks.
-    :param control_mode:
+    :param estimator_status_flags:
     :param estimator_status:
     :param b_pos_only_when_sensors_fused:
     :return: a tuple of two lists that contain strings for the std checks and for the innovation
@@ -97,7 +102,7 @@ def find_checks_that_apply(
     innov_fail_checks.append('posv')
 
     # Magnetometer Sensor Checks
-    if (np.amax(control_mode['yaw_aligned']) > 0.5):
+    if (np.amax(estimator_status_flags['cs_yaw_align']) > 0.5):
         sensor_checks.append('mag')
 
         innov_fail_checks.append('magx')
@@ -106,13 +111,14 @@ def find_checks_that_apply(
         innov_fail_checks.append('yaw')
 
     # Velocity Sensor Checks
-    if (np.amax(control_mode['using_gps']) > 0.5):
+    if (np.amax(estimator_status_flags['cs_gps']) > 0.5):
         sensor_checks.append('vel')
-        innov_fail_checks.append('vel')
+        innov_fail_checks.append('velh')
+        innov_fail_checks.append('velv')
 
     # Position Sensor Checks
-    if (pos_checks_when_sensors_not_fused or (np.amax(control_mode['using_gps']) > 0.5)
-        or (np.amax(control_mode['using_evpos']) > 0.5)):
+    if (pos_checks_when_sensors_not_fused or (np.amax(estimator_status_flags['cs_gps']) > 0.5)
+        or (np.amax(estimator_status_flags['cs_ev_pos']) > 0.5)):
         sensor_checks.append('pos')
         innov_fail_checks.append('posh')
 
@@ -128,7 +134,7 @@ def find_checks_that_apply(
         innov_fail_checks.append('hagl')
 
     # optical flow sensor checks
-    if (np.amax(control_mode['using_optflow']) > 0.5):
+    if (np.amax(estimator_status_flags['cs_opt_flow']) > 0.5):
         innov_fail_checks.append('ofx')
         innov_fail_checks.append('ofy')
 

Tools/ecl_ekf/analysis/checks.py

@@ -123,7 +123,8 @@ def perform_sensor_innov_checks(
                                               ('magy', 'magy_fail_percentage', 'mag'),
                                               ('magz', 'magz_fail_percentage', 'mag'),
                                               ('yaw', 'yaw_fail_percentage', 'yaw'),
-                                              ('vel', 'vel_fail_percentage', 'vel'),
+                                              ('velh', 'vel_fail_percentage', 'vel'),
+                                              ('velv', 'vel_fail_percentage', 'vel'),
                                               ('posh', 'pos_fail_percentage', 'pos'),
                                               ('tas', 'tas_fail_percentage', 'tas'),
                                               ('hagl', 'hagl_fail_percentage', 'hagl'),

Tools/ecl_ekf/analysis/metrics.py

@@ -11,7 +11,7 @@ import numpy as np
 from analysis.detectors import InAirDetector
 
 def calculate_ecl_ekf_metrics(
-        ulog: ULog, innov_flags: Dict[str, float], innov_fail_checks: List[str],
+        ulog: ULog, estimator_status_flags: Dict[str, float], innov_fail_checks: List[str],
         sensor_checks: List[str], in_air: InAirDetector, in_air_no_ground_effects: InAirDetector,
         multi_instance: int = 0, red_thresh: float = 1.0, amb_thresh: float = 0.5) -> Tuple[dict, dict, dict, dict]:
 
@@ -20,7 +20,7 @@ def calculate_ecl_ekf_metrics(
         red_thresh=red_thresh, amb_thresh=amb_thresh)
 
     innov_fail_metrics = calculate_innov_fail_metrics(
-        innov_flags, innov_fail_checks, in_air, in_air_no_ground_effects)
+        estimator_status_flags, innov_fail_checks, in_air, in_air_no_ground_effects)
 
     imu_metrics = calculate_imu_metrics(ulog, multi_instance, in_air_no_ground_effects)
 
@@ -90,10 +90,10 @@ def calculate_sensor_metrics(
 
 
 def calculate_innov_fail_metrics(
-        innov_flags: dict, innov_fail_checks: List[str], in_air: InAirDetector,
+        estimator_status_flags: dict, innov_fail_checks: List[str], in_air: InAirDetector,
         in_air_no_ground_effects: InAirDetector) -> dict:
     """
-    :param innov_flags:
+    :param estimator_status_flags:
     :param innov_fail_checks:
     :param in_air:
     :param in_air_no_ground_effects:
@@ -103,17 +103,18 @@ def calculate_innov_fail_metrics(
     innov_fail_metrics = dict()
 
     # calculate innovation check fail metrics
-    for signal_id, signal, result in [('posv', 'posv_innov_fail', 'hgt_fail_percentage'),
-                                      ('magx', 'magx_innov_fail', 'magx_fail_percentage'),
-                                      ('magy', 'magy_innov_fail', 'magy_fail_percentage'),
-                                      ('magz', 'magz_innov_fail', 'magz_fail_percentage'),
-                                      ('yaw', 'yaw_innov_fail', 'yaw_fail_percentage'),
-                                      ('vel', 'vel_innov_fail', 'vel_fail_percentage'),
-                                      ('posh', 'posh_innov_fail', 'pos_fail_percentage'),
-                                      ('tas', 'tas_innov_fail', 'tas_fail_percentage'),
-                                      ('hagl', 'hagl_innov_fail', 'hagl_fail_percentage'),
-                                      ('ofx', 'ofx_innov_fail', 'ofx_fail_percentage'),
-                                      ('ofy', 'ofy_innov_fail', 'ofy_fail_percentage')]:
+    for signal_id, signal, result in [('posv', 'reject_ver_pos', 'hgt_fail_percentage'),
+                                      ('magx', 'reject_mag_x', 'magx_fail_percentage'),
+                                      ('magy', 'reject_mag_y', 'magy_fail_percentage'),
+                                      ('magz', 'reject_mag_z', 'magz_fail_percentage'),
+                                      ('yaw', 'reject_yaw', 'yaw_fail_percentage'),
+                                      ('velh', 'reject_hor_vel', 'vel_fail_percentage'),
+                                      ('velv', 'reject_ver_vel', 'vel_fail_percentage'),
+                                      ('posh', 'reject_hor_pos', 'pos_fail_percentage'),
+                                      ('tas', 'reject_airspeed', 'tas_fail_percentage'),
+                                      ('hagl', 'reject_hagl', 'hagl_fail_percentage'),
+                                      ('ofx', 'reject_optflow_x', 'ofx_fail_percentage'),
+                                      ('ofy', 'reject_optflow_y', 'ofy_fail_percentage')]:
 
         # only run innov fail checks, if they apply.
         if signal_id in innov_fail_checks:
@@ -125,7 +126,7 @@ def calculate_innov_fail_metrics(
                 in_air_detector = in_air
 
             innov_fail_metrics[result] = calculate_stat_from_signal(
-                innov_flags, 'estimator_status', signal, in_air_detector,
+                estimator_status_flags, 'estimator_status_flags', signal, in_air_detector,
                 lambda x: 100.0 * np.mean(x > 0.5))
 
     return innov_fail_metrics
@@ -152,7 +153,7 @@ def calculate_imu_metrics(ulog: ULog, multi_instance, in_air_no_ground_effects:
 
             if vehicle_imu_status_data['accel_device_id'][0] == estimator_status_data['accel_device_id'][0]:
 
-                for signal, result in [('delta_angle_coning_metric', 'imu_coning'),
+                for signal, result in [('gyro_coning_vibration', 'imu_coning'),
                                        ('gyro_vibration_metric', 'imu_hfgyro'),
                                        ('accel_vibration_metric', 'imu_hfaccel')]:
 

Tools/ecl_ekf/analysis/post_processing.py

@@ -7,115 +7,6 @@ from typing import Tuple
 
 import numpy as np
 
-
-def get_estimator_check_flags(estimator_status: dict) -> Tuple[dict, dict, dict]:
-    """
-    :param estimator_status:
-    :return:
-    """
-    control_mode = get_control_mode_flags(estimator_status)
-    innov_flags = get_innovation_check_flags(estimator_status)
-    gps_fail_flags = get_gps_check_fail_flags(estimator_status)
-    return control_mode, innov_flags, gps_fail_flags
-
-
-def get_control_mode_flags(estimator_status: dict) -> dict:
-    """
-    :param estimator_status:
-    :return:
-    """
-
-    control_mode = dict()
-    # extract control mode metadata from estimator_status.control_mode_flags
-    # 0 - true if the filter tilt alignment is complete
-    # 1 - true if the filter yaw alignment is complete
-    # 2 - true if GPS measurements are being fused
-    # 3 - true if optical flow measurements are being fused
-    # 4 - true if a simple magnetic yaw heading is being fused
-    # 5 - true if 3-axis magnetometer measurement are being fused
-    # 6 - true if synthetic magnetic declination measurements are being fused
-    # 7 - true when the vehicle is airborne
-    # 8 - true when wind velocity is being estimated
-    # 9 - true when baro height is being fused as a primary height reference
-    # 10 - true when range finder height is being fused as a primary height reference
-    # 11 - true when range finder height is being fused as a primary height reference
-    # 12 - true when local position data from external vision is being fused
-    # 13 - true when yaw data from external vision measurements is being fused
-    # 14 - true when height data from external vision measurements is being fused
-    # 15 - true when synthetic sideslip measurements are being fused
-    # 16 - true true when the mag field does not match the expected strength
-    # 17 - true true when the vehicle is operating as a fixed wing vehicle
-    # 18 - true when the magnetometer has been declared faulty and is no longer being used
-    # 19 - true true when airspeed measurements are being fused
-    # 20 - true true when protection from ground effect induced static pressure rise is active
-    # 21 - true when rng data wasn't ready for more than 10s and new rng values haven't changed enough
-    # 22 - true when yaw (not ground course) data from a GPS receiver is being fused
-    # 23 - true when the in-flight mag field alignment has been completed
-    # 24 - true when local earth frame velocity data from external vision measurements are being fused
-    # 25 - true when we are using a synthesized measurement for the magnetometer Z component
-    control_mode['tilt_aligned'] = ((2 ** 0 & estimator_status['control_mode_flags']) > 0) * 1
-    control_mode['yaw_aligned'] = ((2 ** 1 & estimator_status['control_mode_flags']) > 0) * 1
-    control_mode['using_gps'] = ((2 ** 2 & estimator_status['control_mode_flags']) > 0) * 1
-    control_mode['using_optflow'] = ((2 ** 3 & estimator_status['control_mode_flags']) > 0) * 1
-    control_mode['using_magyaw'] = ((2 ** 4 & estimator_status['control_mode_flags']) > 0) * 1
-    control_mode['using_mag3d'] = ((2 ** 5 & estimator_status['control_mode_flags']) > 0) * 1
-    control_mode['using_magdecl'] = ((2 ** 6 & estimator_status['control_mode_flags']) > 0) * 1
-    control_mode['airborne'] = ((2 ** 7 & estimator_status['control_mode_flags']) > 0) * 1
-    control_mode['estimating_wind'] = ((2 ** 8 & estimator_status['control_mode_flags']) > 0) * 1
-    control_mode['using_barohgt'] = ((2 ** 9 & estimator_status['control_mode_flags']) > 0) * 1
-    control_mode['using_rnghgt'] = ((2 ** 10 & estimator_status['control_mode_flags']) > 0) * 1
-    control_mode['using_gpshgt'] = ((2 ** 11 & estimator_status['control_mode_flags']) > 0) * 1
-    control_mode['using_evpos'] = ((2 ** 12 & estimator_status['control_mode_flags']) > 0) * 1
-    control_mode['using_evyaw'] = ((2 ** 13 & estimator_status['control_mode_flags']) > 0) * 1
-    control_mode['using_evhgt'] = ((2 ** 14 & estimator_status['control_mode_flags']) > 0) * 1
-    control_mode['fuse_beta'] = ((2 ** 15 & estimator_status['control_mode_flags']) > 0) * 1
-    control_mode['mag_field_disturbed'] = ((2 ** 16 & estimator_status['control_mode_flags']) > 0) * 1
-    control_mode['fixed_wing'] = ((2 ** 17 & estimator_status['control_mode_flags']) > 0) * 1
-    control_mode['mag_fault'] = ((2 ** 18 & estimator_status['control_mode_flags']) > 0) * 1
-    control_mode['fuse_aspd'] = ((2 ** 19 & estimator_status['control_mode_flags']) > 0) * 1
-    control_mode['gnd_effect'] = ((2 ** 20 & estimator_status['control_mode_flags']) > 0) * 1
-    control_mode['rng_stuck'] = ((2 ** 21 & estimator_status['control_mode_flags']) > 0) * 1
-    control_mode['gps_yaw'] = ((2 ** 22 & estimator_status['control_mode_flags']) > 0) * 1
-    control_mode['mag_aligned_in_flight'] = ((2 ** 23 & estimator_status['control_mode_flags']) > 0) * 1
-    control_mode['ev_vel'] = ((2 ** 24 & estimator_status['control_mode_flags']) > 0) * 1
-    control_mode['synthetic_mag_z'] = ((2 ** 25 & estimator_status['control_mode_flags']) > 0) * 1
-    return control_mode
-
-def get_innovation_check_flags(estimator_status: dict) -> dict:
-    """
-    :param estimator_status:
-    :return:
-    """
-
-    innov_flags = dict()
-    # innovation_check_flags summary
-    # 0 - true if velocity observations have been rejected
-    # 1 - true if horizontal position observations have been rejected
-    # 2 - true if true if vertical position observations have been rejected
-    # 3 - true if the X magnetometer observation has been rejected
-    # 4 - true if the Y magnetometer observation has been rejected
-    # 5 - true if the Z magnetometer observation has been rejected
-    # 6 - true if the yaw observation has been rejected
-    # 7 - true if the airspeed observation has been rejected
-    # 8 - true if synthetic sideslip observation has been rejected
-    # 9 - true if the height above ground observation has been rejected
-    # 10 - true if the X optical flow observation has been rejected
-    # 11 - true if the Y optical flow observation has been rejected
-    innov_flags['vel_innov_fail'] = ((2 ** 0 & estimator_status['innovation_check_flags']) > 0) * 1
-    innov_flags['posh_innov_fail'] = ((2 ** 1 & estimator_status['innovation_check_flags']) > 0) * 1
-    innov_flags['posv_innov_fail'] = ((2 ** 2 & estimator_status['innovation_check_flags']) > 0) * 1
-    innov_flags['magx_innov_fail'] = ((2 ** 3 & estimator_status['innovation_check_flags']) > 0) * 1
-    innov_flags['magy_innov_fail'] = ((2 ** 4 & estimator_status['innovation_check_flags']) > 0) * 1
-    innov_flags['magz_innov_fail'] = ((2 ** 5 & estimator_status['innovation_check_flags']) > 0) * 1
-    innov_flags['yaw_innov_fail'] = ((2 ** 6 & estimator_status['innovation_check_flags']) > 0) * 1
-    innov_flags['tas_innov_fail'] = ((2 ** 7 & estimator_status['innovation_check_flags']) > 0) * 1
-    innov_flags['sli_innov_fail'] = ((2 ** 8 & estimator_status['innovation_check_flags']) > 0) * 1
-    innov_flags['hagl_innov_fail'] = ((2 ** 9 & estimator_status['innovation_check_flags']) > 0) * 1
-    innov_flags['ofx_innov_fail'] = ((2 ** 10 & estimator_status['innovation_check_flags']) > 0) * 1
-    innov_flags['ofy_innov_fail'] = ((2 ** 11 & estimator_status['innovation_check_flags']) > 0) * 1
-    return innov_flags
-
-
 def get_gps_check_fail_flags(estimator_status: dict) -> dict:
     """
     :param estimator_status:

Tools/ecl_ekf/plotting/pdf_report.py

@@ -11,7 +11,7 @@ import numpy as np
 from matplotlib.backends.backend_pdf import PdfPages
 from pyulog import ULog
 
-from analysis.post_processing import magnetic_field_estimates_from_states, get_estimator_check_flags
+from analysis.post_processing import magnetic_field_estimates_from_states, get_gps_check_fail_flags
 from plotting.data_plots import TimeSeriesPlot, InnovationPlot, ControlModeSummaryPlot, \
     CheckFlagsPlot
 from analysis.detectors import PreconditionError
@@ -33,6 +33,11 @@ def create_pdf_report(ulog: ULog, multi_instance: int, output_plot_filename: str
     except:
         raise PreconditionError('could not find estimator_status instance', multi_instance)
 
+    try:
+        estimator_status_flags = ulog.get_dataset('estimator_status_flags', multi_instance).data
+    except:
+        raise PreconditionError('could not find estimator_status_flags instance', multi_instance)
+
     try:
         estimator_states = ulog.get_dataset('estimator_states', multi_instance).data
     except:
@@ -68,12 +73,13 @@ def create_pdf_report(ulog: ULog, multi_instance: int, output_plot_filename: str
     except:
         raise PreconditionError('could not find innovation data')
 
-    control_mode, innov_flags, gps_fail_flags = get_estimator_check_flags(estimator_status)
+    gps_fail_flags = get_gps_check_fail_flags(estimator_status)
 
     status_time = 1e-6 * estimator_status['timestamp']
+    status_flags_time = 1e-6 * estimator_status_flags['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)
+    on_ground_transition_time = detect_airtime(estimator_status_flags, status_flags_time)
 
     with PdfPages(output_plot_filename) as pdf_pages:
 
@@ -173,9 +179,9 @@ def create_pdf_report(ulog: ULog, multi_instance: int, output_plot_filename: str
 
         # 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']],
+            status_flags_time, estimator_status_flags, [['cs_tilt_align', 'cs_yaw_align'],
+            ['cs_gps', 'cs_opt_flow', 'cs_ev_pos'], ['cs_baro_hgt', 'cs_gps_hgt',
+             'cs_rng_hgt', 'cs_ev_hgt'], ['cs_mag_hdg', 'cs_mag_3d', 'cs_mag_dec']],
             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',
@@ -188,7 +194,7 @@ def create_pdf_report(ulog: ULog, multi_instance: int, output_plot_filename: str
         # 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:
+        if np.amin(np.diff(estimator_status_flags['cs_in_air'])) > -0.5:
             airborne_annotations.append(
                 (on_ground_transition_time, 'air to ground transition not detected'))
         else:
@@ -197,7 +203,7 @@ def create_pdf_report(ulog: ULog, multi_instance: int, output_plot_filename: str
         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:
+        if np.amax(np.diff(estimator_status_flags['cs_in_air'])) < 0.5:
             airborne_annotations.append(
                 (in_air_transition_time, 'ground to air transition not detected'))
         else:
@@ -205,7 +211,7 @@ def create_pdf_report(ulog: ULog, multi_instance: int, output_plot_filename: str
                 (in_air_transition_time, 'in-air at {:.1f} sec'.format(in_air_transition_time)))
 
         data_plot = ControlModeSummaryPlot(
-            status_time, control_mode, [['airborne'], ['estimating_wind']],
+            status_flags_time, estimator_status_flags, [['cs_in_air'], ['cs_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)
@@ -214,15 +220,15 @@ def create_pdf_report(ulog: ULog, multi_instance: int, output_plot_filename: str
 
         # 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)',
+            status_flags_time, estimator_status_flags, [['reject_hor_vel', 'reject_hor_pos'], ['reject_ver_vel', 'reject_ver_pos',
+                                                                               'reject_hagl'],
+                                       ['reject_mag_x', 'reject_mag_y', 'reject_mag_z',
+                                        'reject_yaw'], ['reject_airspeed'], ['reject_sideslip'],
+                                       ['reject_optflow_x',
+                                        'reject_optflow_y']], 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'],
+            legend=[['vel NE', 'pos NE'], ['vel D', '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)
@@ -344,33 +350,33 @@ def create_pdf_report(ulog: ULog, multi_instance: int, output_plot_filename: str
         data_plot.close()
 
 
-def detect_airtime(control_mode, status_time):
+def detect_airtime(estimator_status_flags, status_flags_time):
     # define flags for starting and finishing in air
     b_starts_in_air = False
     b_finishes_in_air = False
     # calculate in-air transition time
-    if (np.amin(control_mode['airborne']) < 0.5) and (np.amax(control_mode['airborne']) > 0.5):
-        in_air_transtion_time_arg = np.argmax(np.diff(control_mode['airborne']))
-        in_air_transition_time = status_time[in_air_transtion_time_arg]
-    elif (np.amax(control_mode['airborne']) > 0.5):
-        in_air_transition_time = np.amin(status_time)
+    if (np.amin(estimator_status_flags['cs_in_air']) < 0.5) and (np.amax(estimator_status_flags['cs_in_air']) > 0.5):
+        in_air_transtion_time_arg = np.argmax(np.diff(estimator_status_flags['cs_in_air']))
+        in_air_transition_time = status_flags_time[in_air_transtion_time_arg]
+    elif (np.amax(estimator_status_flags['cs_in_air']) > 0.5):
+        in_air_transition_time = np.amin(status_flags_time)
         print('log starts while in-air at ' + str(round(in_air_transition_time, 1)) + ' sec')
         b_starts_in_air = True
     else:
         in_air_transition_time = float('NaN')
         print('always on ground')
     # calculate on-ground transition time
-    if (np.amin(np.diff(control_mode['airborne'])) < 0.0):
-        on_ground_transition_time_arg = np.argmin(np.diff(control_mode['airborne']))
-        on_ground_transition_time = status_time[on_ground_transition_time_arg]
-    elif (np.amax(control_mode['airborne']) > 0.5):
-        on_ground_transition_time = np.amax(status_time)
+    if (np.amin(np.diff(estimator_status_flags['cs_in_air'])) < 0.0):
+        on_ground_transition_time_arg = np.argmin(np.diff(estimator_status_flags['cs_in_air']))
+        on_ground_transition_time = status_flags_time[on_ground_transition_time_arg]
+    elif (np.amax(estimator_status_flags['cs_in_air']) > 0.5):
+        on_ground_transition_time = np.amax(status_flags_time)
         print('log finishes while in-air at ' + str(round(on_ground_transition_time, 1)) + ' sec')
         b_finishes_in_air = True
     else:
         on_ground_transition_time = float('NaN')
         print('always on ground')
-    if (np.amax(np.diff(control_mode['airborne'])) > 0.5) and (np.amin(np.diff(control_mode['airborne'])) < -0.5):
+    if (np.amax(np.diff(estimator_status_flags['cs_in_air'])) > 0.5) and (np.amin(np.diff(estimator_status_flags['cs_in_air'])) < -0.5):
         if ((on_ground_transition_time - in_air_transition_time) > 0.0):
             in_air_duration = on_ground_transition_time - in_air_transition_time
         else: