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Tools/ecl_ekf: Update EKF log analysis

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Add assessment of IMU bias and mag field estimation
Reduce warning false positives by adjusting thresholds and eliminating use of peak value plots for output observer monitoring
Clear each figure after saving to reduce memory usage
This commit is contained in:
Paul Riseborough
2017-04-21 08:34:19 +02:00
committed by Lorenz Meier
parent ed9a9b772e
commit 2a34bde0e9
3 changed files with 192 additions and 95 deletions
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Tools/ecl_ekf/batch_process_metadata_ekf.py
+37 -58
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@@ -94,12 +94,9 @@ population_results = {
'imu_hfdang_mean_avg':[float('NaN'),'The mean of the mean in-flight value of the IMU delta hihg frequency delta angle vibration level (mrad)'],
'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)'],
'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)'],
'obs_ang_max_avg':[float('NaN'),'The mean of the maximum in-flight values of the output observer angular tracking error magnitude (mrad)'],
'obs_ang_mean_avg':[float('NaN'),'The mean of the mean in-flight value of the output observer angular tracking error magnitude (mrad)'],
'obs_vel_max_avg':[float('NaN'),'The mean of the maximum in-flight values of the output observer velocity tracking error magnitude (m/s)'],
'obs_vel_mean_avg':[float('NaN'),'The mean of the mean in-flight value of the output observer velocity tracking error magnitude (m/s)'],
'obs_pos_max_avg':[float('NaN'),'The mean of the maximum in-flight values of the output observer position tracking error magnitude (m)'],
'obs_pos_mean_avg':[float('NaN'),'The mean of the mean in-flight value of the output observer position tracking error magnitude (m)'],
'obs_ang_median_avg':[float('NaN'),'The mean of the median in-flight value of the output observer angular tracking error magnitude (mrad)'],
'obs_vel_median_avg':[float('NaN'),'The mean of the median in-flight value of the output observer velocity tracking error magnitude (m/s)'],
'obs_pos_median_avg':[float('NaN'),'The mean of the median in-flight value of the output observer position tracking error magnitude (m)'],
}
# get population summary statistics
@@ -171,6 +168,7 @@ if (len(result1) > 0 and len(result2) > 0):
plt.ylabel("Frequency")
pp.savefig()
plt.close(1)
# Velocity Sensor (GPS)
temp = np.asarray([population_data[k].get('vel_test_max') for k in found_keys])
@@ -197,6 +195,7 @@ if (len(result1) > 0 and len(result2) > 0):
plt.ylabel("Frequency")
pp.savefig()
plt.close(2)
# Position Sensor (GPS or external vision)
temp = np.asarray([population_data[k].get('pos_test_max') for k in found_keys])
@@ -223,6 +222,7 @@ if (len(result1) > 0 and len(result2) > 0):
plt.ylabel("Frequency")
pp.savefig()
plt.close(3)
# Height Sensor
temp = np.asarray([population_data[k].get('hgt_test_max') for k in found_keys])
@@ -249,6 +249,7 @@ if (len(result1) > 0 and len(result2) > 0):
plt.ylabel("Frequency")
pp.savefig()
plt.close(4)
# Airspeed Sensor
temp = np.asarray([population_data[k].get('tas_test_max') for k in found_keys])
@@ -275,6 +276,7 @@ if (len(result1) > 0 and len(result2) > 0):
plt.ylabel("Frequency")
pp.savefig()
plt.close(5)
# Height Above Ground Sensor
temp = np.asarray([population_data[k].get('hagl_test_max') for k in found_keys])
@@ -301,6 +303,7 @@ if (len(result1) > 0 and len(result2) > 0):
plt.ylabel("Frequency")
pp.savefig()
plt.close(6)
# Optical Flow Sensor
temp = np.asarray([population_data[k].get('ofx_fail_percentage') for k in found_keys])
@@ -327,7 +330,7 @@ if (len(result1) > 0 and len(result2) > 0):
plt.ylabel("Frequency")
pp.savefig()
plt.close(7)
# IMU coning vibration levels
temp = np.asarray([population_data[k].get('imu_coning_peak') for k in found_keys])
@@ -354,6 +357,7 @@ if (len(result1) > 0 and len(result2) > 0):
plt.ylabel("Frequency")
pp.savefig()
plt.close(8)
# IMU high frequency delta angle vibration levels
temp = np.asarray([population_data[k].get('imu_hfdang_peak') for k in found_keys])
@@ -380,6 +384,7 @@ if (len(result1) > 0 and len(result2) > 0):
plt.ylabel("Frequency")
pp.savefig()
plt.close(9)
# IMU high frequency delta velocity vibration levels
temp = np.asarray([population_data[k].get('imu_hfdvel_peak') for k in found_keys])
@@ -406,84 +411,58 @@ if (len(result1) > 0 and len(result2) > 0):
plt.ylabel("Frequency")
pp.savefig()
plt.close(10)
# Output Observer Angular Tracking
temp = np.asarray([population_data[k].get('output_obs_ang_err_peak') for k in found_keys])
result1 = 1000.0 * temp[np.isfinite(temp)]
temp = np.asarray([population_data[k].get('output_obs_ang_err_mean') for k in found_keys])
result2 = 1000.0 * temp[np.isfinite(temp)]
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(result1) > 0 and len(result2) > 0):
population_results['obs_ang_max_avg'][0] = np.mean(result1)
population_results['obs_ang_mean_avg'][0] = np.mean(result2)
if (len(result) > 0):
population_results['obs_ang_median_avg'][0] = np.mean(result)
plt.figure(11,figsize=(20,13))
plt.subplot(2,1,1)
plt.hist(result1)
plt.title("Gaussian Histogram - Output Observer Angular Tracking Error Peak")
plt.xlabel("output_obs_ang_err_peak (mrad)")
plt.ylabel("Frequency")
plt.subplot(2,1,2)
plt.hist(result2)
plt.title("Gaussian Histogram - Output Observer Angular Tracking Error Mean")
plt.xlabel("output_obs_ang_err_mean (mrad)")
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_peak') for k in found_keys])
result1 = temp[np.isfinite(temp)]
temp = np.asarray([population_data[k].get('output_obs_vel_err_mean') for k in found_keys])
result2 = temp[np.isfinite(temp)]
temp = np.asarray([population_data[k].get('output_obs_vel_err_median') for k in found_keys])
result = temp[np.isfinite(temp)]
if (len(result1) > 0 and len(result2) > 0):
population_results['obs_vel_max_avg'][0] = np.mean(result1)
population_results['obs_vel_mean_avg'][0] = np.mean(result2)
if (len(result) > 0):
population_results['obs_vel_median_avg'][0] = np.mean(result)
plt.figure(12,figsize=(20,13))
plt.subplot(2,1,1)
plt.hist(result1)
plt.title("Gaussian Histogram - Output Observer Velocity Tracking Error Peak")
plt.xlabel("output_obs_ang_err_peak (m/s)")
plt.ylabel("Frequency")
plt.subplot(2,1,2)
plt.hist(result2)
plt.title("Gaussian Histogram - Output Observer Velocity Tracking Error Mean")
plt.xlabel("output_obs_ang_err_mean (m/s)")
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_peak') for k in found_keys])
result1 = temp[np.isfinite(temp)]
temp = np.asarray([population_data[k].get('output_obs_pos_err_mean') for k in found_keys])
result2 = temp[np.isfinite(temp)]
temp = np.asarray([population_data[k].get('output_obs_pos_err_median') for k in found_keys])
result = temp[np.isfinite(temp)]
if (len(result1) > 0 and len(result2) > 0):
population_results['obs_pos_max_avg'][0] = np.mean(result1)
population_results['obs_pos_mean_avg'][0] = np.mean(result2)
if (len(result) > 0):
population_results['obs_pos_median_avg'][0] = np.mean(result)
plt.figure(13,figsize=(20,13))
plt.subplot(2,1,1)
plt.hist(result1)
plt.title("Gaussian Histogram - Output Observer Position Tracking Error Peak")
plt.xlabel("output_obs_ang_err_peak (m)")
plt.ylabel("Frequency")
plt.subplot(2,1,2)
plt.hist(result2)
plt.title("Gaussian Histogram - Output Observer Position Tracking Error Mean")
plt.xlabel("output_obs_ang_err_mean (m)")
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)
# close the pdf file
pp.close()