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ecl-ekf log analysis tool: fix missing filter_fault_status and one-off index bug #9552

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May 29, 2018
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ecl-ekf log analysis tool: fix missing filter_fault_status and one-off index bug #9552

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82 changes: 42 additions & 40 deletions Tools/ecl_ekf/analyse_logdata_ekf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1035,6 +1035,8 @@ def analyse_ekf(estimator_status, ekf2_innovations, sensor_preflight, check_leve
'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'],
'flow_sensor_status': ['Pass',
'Optical Flow 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'],
'filter_fault_status': ['Pass',
'Internal Filter 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_percentage_red': [float('NaN'),
'The percentage of in-flight consolidated magnetic field sensor innovation consistency test values > 1.0.'],
'mag_percentage_amber': [float('NaN'),
Expand Down Expand Up @@ -1138,105 +1140,105 @@ def analyse_ekf(estimator_status, ekf2_innovations, sensor_preflight, check_leve
if (innov_early_end_index > (innov_late_start_index + 100)):
# Output Observer Tracking Errors
test_results['output_obs_ang_err_median'][0] = np.median(
ekf2_innovations['output_tracking_error[0]'][innov_late_start_index:innov_early_end_index])
ekf2_innovations['output_tracking_error[0]'][innov_late_start_index:innov_early_end_index + 1])
test_results['output_obs_vel_err_median'][0] = np.median(
ekf2_innovations['output_tracking_error[1]'][innov_late_start_index:innov_early_end_index])
ekf2_innovations['output_tracking_error[1]'][innov_late_start_index:innov_early_end_index + 1])
test_results['output_obs_pos_err_median'][0] = np.median(
ekf2_innovations['output_tracking_error[2]'][innov_late_start_index:innov_early_end_index])
ekf2_innovations['output_tracking_error[2]'][innov_late_start_index:innov_early_end_index + 1])
# reduction of status message data
if (early_end_index > (late_start_index + 100)):
# IMU vibration checks
temp = np.amax(estimator_status['vibe[0]'][late_start_index:early_end_index])
if (temp > 0.0):
test_results['imu_coning_peak'][0] = temp
test_results['imu_coning_mean'][0] = np.mean(estimator_status['vibe[0]'][late_start_index:early_end_index])
test_results['imu_coning_mean'][0] = np.mean(estimator_status['vibe[0]'][late_start_index:early_end_index + 1])
temp = np.amax(estimator_status['vibe[1]'][late_start_index:early_end_index])
if (temp > 0.0):
test_results['imu_hfdang_peak'][0] = temp
test_results['imu_hfdang_mean'][0] = np.mean(estimator_status['vibe[1]'][late_start_index:early_end_index])
test_results['imu_hfdang_mean'][0] = np.mean(estimator_status['vibe[1]'][late_start_index:early_end_index + 1])
temp = np.amax(estimator_status['vibe[2]'][late_start_index:early_end_index])
if (temp > 0.0):
test_results['imu_hfdvel_peak'][0] = temp
test_results['imu_hfdvel_mean'][0] = np.mean(estimator_status['vibe[2]'][late_start_index:early_end_index])
test_results['imu_hfdvel_mean'][0] = np.mean(estimator_status['vibe[2]'][late_start_index:early_end_index + 1])

# Magnetometer Sensor Checks
if (np.amax(yaw_aligned) > 0.5):
mag_num_red = (estimator_status['mag_test_ratio'][start_index:end_index] > 1.0).sum()
mag_num_amber = (estimator_status['mag_test_ratio'][start_index:end_index] > 0.5).sum() - mag_num_red
mag_num_red = (estimator_status['mag_test_ratio'][start_index:end_index + 1] > 1.0).sum()
mag_num_amber = (estimator_status['mag_test_ratio'][start_index:end_index + 1] > 0.5).sum() - mag_num_red
test_results['mag_percentage_red'][0] = 100.0 * mag_num_red / num_valid_values_trimmed
test_results['mag_percentage_amber'][0] = 100.0 * mag_num_amber / num_valid_values_trimmed
test_results['mag_test_max'][0] = np.amax(
estimator_status['mag_test_ratio'][late_start_index:early_end_index])
estimator_status['mag_test_ratio'][late_start_index:early_end_index + 1])
test_results['mag_test_mean'][0] = np.mean(estimator_status['mag_test_ratio'][start_index:end_index])
test_results['magx_fail_percentage'][0] = 100.0 * (
magx_innov_fail[late_start_index:early_end_index] > 0.5).sum() / num_valid_values_trimmed
magx_innov_fail[late_start_index:early_end_index + 1] > 0.5).sum() / num_valid_values_trimmed
test_results['magy_fail_percentage'][0] = 100.0 * (
magy_innov_fail[late_start_index:early_end_index] > 0.5).sum() / num_valid_values_trimmed
magy_innov_fail[late_start_index:early_end_index + 1] > 0.5).sum() / num_valid_values_trimmed
test_results['magz_fail_percentage'][0] = 100.0 * (
magz_innov_fail[late_start_index:early_end_index] > 0.5).sum() / num_valid_values_trimmed
magz_innov_fail[late_start_index:early_end_index + 1] > 0.5).sum() / num_valid_values_trimmed
test_results['yaw_fail_percentage'][0] = 100.0 * (
yaw_innov_fail[late_start_index:early_end_index] > 0.5).sum() / num_valid_values_trimmed
yaw_innov_fail[late_start_index:early_end_index + 1] > 0.5).sum() / num_valid_values_trimmed

# Velocity Sensor Checks
if (np.amax(using_gps) > 0.5):
vel_num_red = (estimator_status['vel_test_ratio'][start_index:end_index] > 1.0).sum()
vel_num_amber = (estimator_status['vel_test_ratio'][start_index:end_index] > 0.5).sum() - vel_num_red
vel_num_red = (estimator_status['vel_test_ratio'][start_index:end_index + 1] > 1.0).sum()
vel_num_amber = (estimator_status['vel_test_ratio'][start_index:end_index + 1] > 0.5).sum() - vel_num_red
test_results['vel_percentage_red'][0] = 100.0 * vel_num_red / num_valid_values
test_results['vel_percentage_amber'][0] = 100.0 * vel_num_amber / num_valid_values
test_results['vel_test_max'][0] = np.amax(estimator_status['vel_test_ratio'][start_index:end_index])
test_results['vel_test_mean'][0] = np.mean(estimator_status['vel_test_ratio'][start_index:end_index])
test_results['vel_test_max'][0] = np.amax(estimator_status['vel_test_ratio'][start_index:end_index + 1])
test_results['vel_test_mean'][0] = np.mean(estimator_status['vel_test_ratio'][start_index:end_index + 1])
test_results['vel_fail_percentage'][0] = 100.0 * (
vel_innov_fail[start_index:end_index] > 0.5).sum() / num_valid_values
vel_innov_fail[start_index:end_index + 1] > 0.5).sum() / num_valid_values

# Position Sensor Checks
if ((np.amax(using_gps) > 0.5) or (np.amax(using_evpos) > 0.5)):
pos_num_red = (estimator_status['pos_test_ratio'][start_index:end_index] > 1.0).sum()
pos_num_amber = (estimator_status['pos_test_ratio'][start_index:end_index] > 0.5).sum() - pos_num_red
pos_num_red = (estimator_status['pos_test_ratio'][start_index:end_index + 1] > 1.0).sum()
pos_num_amber = (estimator_status['pos_test_ratio'][start_index:end_index + 1] > 0.5).sum() - pos_num_red
test_results['pos_percentage_red'][0] = 100.0 * pos_num_red / num_valid_values
test_results['pos_percentage_amber'][0] = 100.0 * pos_num_amber / num_valid_values
test_results['pos_test_max'][0] = np.amax(estimator_status['pos_test_ratio'][start_index:end_index])
test_results['pos_test_mean'][0] = np.mean(estimator_status['pos_test_ratio'][start_index:end_index])
test_results['pos_test_max'][0] = np.amax(estimator_status['pos_test_ratio'][start_index:end_index + 1])
test_results['pos_test_mean'][0] = np.mean(estimator_status['pos_test_ratio'][start_index:end_index + 1])
test_results['pos_fail_percentage'][0] = 100.0 * (
posh_innov_fail[start_index:end_index] > 0.5).sum() / num_valid_values
posh_innov_fail[start_index:end_index + 1] > 0.5).sum() / num_valid_values

# Height Sensor Checks
hgt_num_red = (estimator_status['hgt_test_ratio'][late_start_index:early_end_index] > 1.0).sum()
hgt_num_amber = (estimator_status['hgt_test_ratio'][late_start_index:early_end_index] > 0.5).sum() - hgt_num_red
hgt_num_red = (estimator_status['hgt_test_ratio'][late_start_index:early_end_index + 1] > 1.0).sum()
hgt_num_amber = (estimator_status['hgt_test_ratio'][late_start_index:early_end_index + 1] > 0.5).sum() - hgt_num_red
test_results['hgt_percentage_red'][0] = 100.0 * hgt_num_red / num_valid_values_trimmed
test_results['hgt_percentage_amber'][0] = 100.0 * hgt_num_amber / num_valid_values_trimmed
test_results['hgt_test_max'][0] = np.amax(estimator_status['hgt_test_ratio'][late_start_index:early_end_index])
test_results['hgt_test_mean'][0] = np.mean(estimator_status['hgt_test_ratio'][late_start_index:early_end_index])
test_results['hgt_test_max'][0] = np.amax(estimator_status['hgt_test_ratio'][late_start_index:early_end_index + 1])
test_results['hgt_test_mean'][0] = np.mean(estimator_status['hgt_test_ratio'][late_start_index:early_end_index + 1])
test_results['hgt_fail_percentage'][0] = 100.0 * (
posv_innov_fail[late_start_index:early_end_index] > 0.5).sum() / num_valid_values_trimmed
posv_innov_fail[late_start_index:early_end_index + 1] > 0.5).sum() / num_valid_values_trimmed

# Airspeed Sensor Checks
if (tas_test_max > 0.0):
tas_num_red = (estimator_status['tas_test_ratio'][start_index:end_index] > 1.0).sum()
tas_num_amber = (estimator_status['tas_test_ratio'][start_index:end_index] > 0.5).sum() - tas_num_red
tas_num_red = (estimator_status['tas_test_ratio'][start_index:end_index + 1] > 1.0).sum()
tas_num_amber = (estimator_status['tas_test_ratio'][start_index:end_index + 1] > 0.5).sum() - tas_num_red
test_results['tas_percentage_red'][0] = 100.0 * tas_num_red / num_valid_values
test_results['tas_percentage_amber'][0] = 100.0 * tas_num_amber / num_valid_values
test_results['tas_test_max'][0] = np.amax(estimator_status['tas_test_ratio'][start_index:end_index])
test_results['tas_test_mean'][0] = np.mean(estimator_status['tas_test_ratio'][start_index:end_index])
test_results['tas_test_max'][0] = np.amax(estimator_status['tas_test_ratio'][start_index:end_index + 1])
test_results['tas_test_mean'][0] = np.mean(estimator_status['tas_test_ratio'][start_index:end_index + 1])
test_results['tas_fail_percentage'][0] = 100.0 * (
tas_innov_fail[start_index:end_index] > 0.5).sum() / num_valid_values
tas_innov_fail[start_index:end_index + 1] > 0.5).sum() / num_valid_values

# HAGL Sensor Checks
if (hagl_test_max > 0.0):
hagl_num_red = (estimator_status['hagl_test_ratio'][start_index:end_index] > 1.0).sum()
hagl_num_amber = (estimator_status['hagl_test_ratio'][start_index:end_index] > 0.5).sum() - hagl_num_red
hagl_num_red = (estimator_status['hagl_test_ratio'][start_index:end_index + 1] > 1.0).sum()
hagl_num_amber = (estimator_status['hagl_test_ratio'][start_index:end_index + 1] > 0.5).sum() - hagl_num_red
test_results['hagl_percentage_red'][0] = 100.0 * hagl_num_red / num_valid_values
test_results['hagl_percentage_amber'][0] = 100.0 * hagl_num_amber / num_valid_values
test_results['hagl_test_max'][0] = np.amax(estimator_status['hagl_test_ratio'][start_index:end_index])
test_results['hagl_test_mean'][0] = np.mean(estimator_status['hagl_test_ratio'][start_index:end_index])
test_results['hagl_test_max'][0] = np.amax(estimator_status['hagl_test_ratio'][start_index:end_index + 1])
test_results['hagl_test_mean'][0] = np.mean(estimator_status['hagl_test_ratio'][start_index:end_index + 1])
test_results['hagl_fail_percentage'][0] = 100.0 * (
hagl_innov_fail[start_index:end_index] > 0.5).sum() / num_valid_values
hagl_innov_fail[start_index:end_index + 1] > 0.5).sum() / num_valid_values

# optical flow sensor checks
if (np.amax(using_optflow) > 0.5):
test_results['ofx_fail_percentage'][0] = 100.0 * (
ofx_innov_fail[late_start_index:early_end_index] > 0.5).sum() / num_valid_values_trimmed
ofx_innov_fail[late_start_index:early_end_index + 1] > 0.5).sum() / num_valid_values_trimmed
test_results['ofy_fail_percentage'][0] = 100.0 * (
ofy_innov_fail[late_start_index:early_end_index] > 0.5).sum() / num_valid_values_trimmed
ofy_innov_fail[late_start_index:early_end_index + 1] > 0.5).sum() / num_valid_values_trimmed

# IMU bias checks
test_results['imu_dang_bias_median'][0] = (np.median(estimator_status['states[10]']) ** 2 + np.median(
Expand Down