Tools/ecl_ekf: Update EKF log analysis

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

Tools/ecl_ekf/batch_process_metadata_ekf.py

@@ -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_median_avg':[float('NaN'),'The mean of the median in-flight value 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_median_avg':[float('NaN'),'The mean of the median in-flight value of the output observer velocity tracking error magnitude (m/s)'],
-'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_pos_median_avg':[float('NaN'),'The mean of the median in-flight value of the output observer position tracking error magnitude (m)'],
-'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)'],
 }
 
 # 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])
+temp = np.asarray([population_data[k].get('output_obs_ang_err_median') for k in found_keys])
-result1 = 1000.0 * temp[np.isfinite(temp)]
+result = 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)]
 
-if (len(result1) > 0 and len(result2) > 0):
+if (len(result) > 0):
-    population_results['obs_ang_max_avg'][0] = np.mean(result1)
+    population_results['obs_ang_median_avg'][0] = np.mean(result)
-    population_results['obs_ang_mean_avg'][0] = np.mean(result2)
 
     plt.figure(11,figsize=(20,13))
 
-    plt.subplot(2,1,1)
+    plt.hist(result)
-    plt.hist(result1)
+    plt.title("Gaussian Histogram - Output Observer Angular Tracking Error Median")
-    plt.title("Gaussian Histogram - Output Observer Angular Tracking Error Peak")
+    plt.xlabel("output_obs_ang_err_median (mrad)")
-    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.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])
+temp = np.asarray([population_data[k].get('output_obs_vel_err_median') for k in found_keys])
-result1 = temp[np.isfinite(temp)]
+result = 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)]
 
-if (len(result1) > 0 and len(result2) > 0):
+if (len(result) > 0):
-    population_results['obs_vel_max_avg'][0] = np.mean(result1)
+    population_results['obs_vel_median_avg'][0] = np.mean(result)
-    population_results['obs_vel_mean_avg'][0] = np.mean(result2)
 
     plt.figure(12,figsize=(20,13))
 
-    plt.subplot(2,1,1)
+    plt.hist(result)
-    plt.hist(result1)
+    plt.title("Gaussian Histogram - Output Observer Velocity Tracking Error Median")
-    plt.title("Gaussian Histogram - Output Observer Velocity Tracking Error Peak")
+    plt.xlabel("output_obs_ang_err_median (m/s)")
-    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.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])
+temp = np.asarray([population_data[k].get('output_obs_pos_err_median') for k in found_keys])
-result1 = temp[np.isfinite(temp)]
+result = 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)]
 
-if (len(result1) > 0 and len(result2) > 0):
+if (len(result) > 0):
-    population_results['obs_pos_max_avg'][0] = np.mean(result1)
+    population_results['obs_pos_median_avg'][0] = np.mean(result)
-    population_results['obs_pos_mean_avg'][0] = np.mean(result2)
 
     plt.figure(13,figsize=(20,13))
 
-    plt.subplot(2,1,1)
+    plt.hist(result)
-    plt.hist(result1)
+    plt.title("Gaussian Histogram - Output Observer Position Tracking Error Median")
-    plt.title("Gaussian Histogram - Output Observer Position Tracking Error Peak")
+    plt.xlabel("output_obs_ang_err_median (m)")
-    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.ylabel("Frequency")
 
     pp.savefig()
+    plt.close(13)
 
 # close the pdf file
 pp.close()

Tools/ecl_ekf/check_level_dict.csv

@@ -18,15 +18,14 @@ pos_amber_warn_pct,5.0
 hgt_amber_warn_pct,5.0
 hagl_amber_warn_pct,5.0
 tas_amber_warn_pct,5.0
-imu_coning_peak_warn,1.0E-5
+imu_coning_peak_warn,1.8E-5
 imu_coning_mean_warn,3.6E-6
-imu_hfdang_peak_warn,2.4E-3
+imu_hfdang_peak_warn,3.0E-3
 imu_hfdang_mean_warn,6.0E-4
-imu_hfdvel_peak_warn,2.5E-2
+imu_hfdvel_peak_warn,1.8E-2
 imu_hfdvel_mean_warn,3.6E-3
-obs_ang_err_peak_warn,9.0E-2
+obs_ang_err_median_warn,8.0E-3
-obs_ang_err_mean_warn,8.0E-3
+obs_vel_err_median_warn,0.05
-obs_vel_err_peak_warn,0.3
+obs_pos_err_median_warn,0.15
-obs_vel_err_mean_warn,0.05
+imu_dang_bias_median_warn,4.4E-4
-obs_pos_err_peak_warn,1.0
+imu_dvel_bias_median_warn,4.8E-3
-obs_pos_err_mean_warn,0.15

Tools/ecl_ekf/process_logdata_ekf.py

@@ -68,6 +68,7 @@ if ('accel_inconsistency_m_s_s' in sensor_preflight.keys()) and ('gyro_inconsist
     plt.ylabel('angular rate (rad/s)')
     plt.xlabel('data index')
     pp.savefig()
+    plt.close(0)
 
 # vertical velocity and position innovations
 plt.figure(1,figsize=(20,13))
@@ -128,7 +129,7 @@ plt.text(innov_5_min_time, innov_5_min, 'min='+s_innov_5_min, fontsize=12, horiz
 #plt.legend(['std='+s_innov_5_std],loc='upper left',frameon=False)
 
 pp.savefig()
-
+plt.close(1)
 # horizontal velocity innovations
 plt.figure(2,figsize=(20,13))
 
@@ -183,6 +184,7 @@ plt.text(innov_1_min_time, innov_1_min, 'min='+s_innov_1_min, fontsize=12, horiz
 #plt.legend(['std='+s_innov_1_std],loc='upper left',frameon=False)
 
 pp.savefig()
+plt.close(2)
 
 # horizontal position innovations
 plt.figure(3,figsize=(20,13))
@@ -239,6 +241,7 @@ plt.text(innov_4_min_time, innov_4_min, 'min='+s_innov_4_min, fontsize=12, horiz
 #plt.legend(['std='+s_innov_4_std],loc='upper left',frameon=False)
 
 pp.savefig()
+plt.close(3)
 
 # manetometer innovations
 plt.figure(4,figsize=(20,13))
@@ -318,6 +321,7 @@ plt.text(innov_2_min_time, innov_2_min, 'min='+s_innov_2_min, fontsize=12, horiz
 #plt.legend(['std='+s_innov_2_std],loc='upper left',frameon=False)
 
 pp.savefig()
+plt.close(4)
 
 # magnetic heading innovations
 plt.figure(5,figsize=(20,13))
@@ -348,6 +352,7 @@ plt.text(innov_0_min_time, innov_0_min, 'min='+s_innov_0_min, fontsize=12, horiz
 #plt.legend(['std='+s_innov_0_std],loc='upper left',frameon=False)
 
 pp.savefig()
+plt.close(5)
 
 # air data innovations
 plt.figure(6,figsize=(20,13))
@@ -400,6 +405,7 @@ plt.text(beta_innov_max_time, beta_innov_max, 'max='+s_beta_innov_max, fontsize=
 plt.text(beta_innov_min_time, beta_innov_min, 'min='+s_beta_innov_min, fontsize=12, horizontalalignment='left', verticalalignment='top')
 
 pp.savefig()
+plt.close(6)
 
 # optical flow innovations
 plt.figure(7,figsize=(20,13))
@@ -455,6 +461,7 @@ plt.text(flow_innov_y_min_time, flow_innov_y_min, 'min='+s_flow_innov_y_min, fon
 #plt.legend(['std='+s_flow_innov_y_std],loc='upper left',frameon=False)
 
 pp.savefig()
+plt.close(7)
 
 # generate metadata for the normalised innovation consistency test levels
 # a value > 1.0 means the measurement data for that test has been rejected by the EKF
@@ -541,6 +548,7 @@ if n_plots == 4:
     plt.text(tas_test_max_time, tas_test_max, 'max='+str(round(tas_test_max,2))+' , mean='+str(round(tas_test_mean,2)), fontsize=12, horizontalalignment='left', verticalalignment='bottom',color='b')
 
 pp.savefig()
+plt.close(8)
 
 # extract control mode metadata from estimator_status.control_mode_flags
 # 0 - true if the filter tilt alignment is complete
@@ -733,6 +741,7 @@ elif np.amax(using_magdecl) > 0:
     plt.text(using_magdecl_time, 0.75, 'magnetic declination aiding at '+str(round(using_magdecl_time,1))+' sec', fontsize=12, horizontalalignment='left', verticalalignment='center',color='g')
 
 pp.savefig()
+plt.close(9)
 
 # control mode summary plot B
 plt.figure(10,figsize=(20,13))
@@ -767,6 +776,7 @@ plt.xlabel('time (sec)')
 plt.grid()
 
 pp.savefig()
+plt.close(10)
 
 # innovation_check_flags summary
 plt.figure(11,figsize=(20,13))
@@ -837,6 +847,7 @@ plt.legend(loc='upper left')
 plt.grid()
 
 pp.savefig()
+plt.close(11)
 
 # gps_check_fail_flags summary
 plt.figure(12,figsize=(20,13))
@@ -884,6 +895,7 @@ plt.legend(loc='upper right')
 plt.grid()
 
 pp.savefig()
+plt.close(12)
 
 # filter reported accuracy
 plt.figure(13,figsize=(20,13))
@@ -896,6 +908,7 @@ plt.legend(loc='upper right')
 plt.grid()
 
 pp.savefig()
+plt.close(13)
 
 # Plot the EKF IMU vibration metrics
 plt.figure(14,figsize=(20,13))
@@ -933,6 +946,7 @@ plt.grid()
 plt.text(vibe_hf_dvel_max_time, vibe_hf_dvel_max, 'max='+str(round(vibe_hf_dvel_max,4)), fontsize=12, horizontalalignment='left', verticalalignment='top')
 
 pp.savefig()
+plt.close(14)
 
 # Plot the EKF output observer tracking errors
 plt.figure(15,figsize=(20,13))
@@ -970,9 +984,114 @@ plt.grid()
 plt.text(pos_track_err_max_time, pos_track_err_max, 'max='+str(round(pos_track_err_max,2)), fontsize=12, horizontalalignment='left', verticalalignment='top')
 
 pp.savefig()
+plt.close(15)
+
+# Plot the delta angle bias estimates
+plt.figure(16,figsize=(20,13))
+
+plt.subplot(3,1,1)
+plt.plot(1e-6*estimator_status['timestamp'] , estimator_status['states[10]'],'b')
+plt.title('Delta Angle Bias Estimates')
+plt.ylabel('X (rad)')
+plt.xlabel('time (sec)')
+plt.grid()
+
+plt.subplot(3,1,2)
+plt.plot(1e-6*estimator_status['timestamp'] , estimator_status['states[11]'],'b')
+plt.ylabel('Y (rad)')
+plt.xlabel('time (sec)')
+plt.grid()
+
+plt.subplot(3,1,3)
+plt.plot(1e-6*estimator_status['timestamp'] , estimator_status['states[12]'],'b')
+plt.ylabel('Z (rad)')
+plt.xlabel('time (sec)')
+plt.grid()
+
+pp.savefig()
+plt.close(16)
+
+# Plot the delta velocity bias estimates
+plt.figure(17,figsize=(20,13))
+
+plt.subplot(3,1,1)
+plt.plot(1e-6*estimator_status['timestamp'] , estimator_status['states[13]'],'b')
+plt.title('Delta Velocity Bias Estimates')
+plt.ylabel('X (m/s)')
+plt.xlabel('time (sec)')
+plt.grid()
+
+plt.subplot(3,1,2)
+plt.plot(1e-6*estimator_status['timestamp'] , estimator_status['states[14]'],'b')
+plt.ylabel('Y (m/s)')
+plt.xlabel('time (sec)')
+plt.grid()
+
+plt.subplot(3,1,3)
+plt.plot(1e-6*estimator_status['timestamp'] , estimator_status['states[15]'],'b')
+plt.ylabel('Z (m/s)')
+plt.xlabel('time (sec)')
+plt.grid()
+
+pp.savefig()
+plt.close(17)
+
+# Plot the earth frame magnetic field estimates
+
+plt.figure(18,figsize=(20,13))
+plt.subplot(3,1,3)
+strength = (estimator_status['states[16]']**2 + estimator_status['states[17]']**2 + estimator_status['states[18]']**2)**0.5
+plt.plot(1e-6*estimator_status['timestamp'] , strength,'b')
+plt.ylabel('strength (Gauss)')
+plt.xlabel('time (sec)')
+plt.grid()
+
+plt.subplot(3,1,1)
+rad2deg = 57.2958
+declination = rad2deg * np.arctan2(estimator_status['states[17]'],estimator_status['states[16]'])
+plt.plot(1e-6*estimator_status['timestamp'] , declination,'b')
+plt.title('Earth Magnetic Field Estimates')
+plt.ylabel('declination (deg)')
+plt.xlabel('time (sec)')
+plt.grid()
+
+plt.subplot(3,1,2)
+inclination = rad2deg * np.arcsin(estimator_status['states[18]'] / strength)
+plt.plot(1e-6*estimator_status['timestamp'] , inclination,'b')
+plt.ylabel('inclination (deg)')
+plt.xlabel('time (sec)')
+plt.grid()
+
+pp.savefig()
+plt.close(18)
+
+# Plot the body frame magnetic field estimates
+plt.figure(19,figsize=(20,13))
+
+plt.subplot(3,1,1)
+plt.plot(1e-6*estimator_status['timestamp'] , estimator_status['states[19]'],'b')
+plt.title('Magnetomer Bias Estimates')
+plt.ylabel('X (Gauss)')
+plt.xlabel('time (sec)')
+plt.grid()
+
+plt.subplot(3,1,2)
+plt.plot(1e-6*estimator_status['timestamp'] , estimator_status['states[20]'],'b')
+plt.ylabel('Y (Gauss)')
+plt.xlabel('time (sec)')
+plt.grid()
+
+plt.subplot(3,1,3)
+plt.plot(1e-6*estimator_status['timestamp'] , estimator_status['states[21]'],'b')
+plt.ylabel('Z (Gauss)')
+plt.xlabel('time (sec)')
+plt.grid()
+
+pp.savefig()
+plt.close(19)
 
 # Plot the EKF wind estimates
-plt.figure(16,figsize=(20,13))
+plt.figure(20,figsize=(20,13))
 
 plt.subplot(2,1,1)
 plt.plot(1e-6*estimator_status['timestamp'] , estimator_status['states[22]'],'b')
@@ -988,6 +1107,7 @@ plt.xlabel('time (sec)')
 plt.grid()
 
 pp.savefig()
+plt.close(20)
 
 # close the pdf file
 pp.close()
@@ -1073,12 +1193,11 @@ test_results = {
 'imu_hfdang_mean':[float('NaN'),'Mean in-flight value of the IMU delta angle high frequency vibration metric (rad)'],
 'imu_hfdvel_peak':[float('NaN'),'Peak in-flight value of the IMU delta velocity high frequency vibration metric (m/s)'],
 'imu_hfdvel_mean':[float('NaN'),'Mean in-flight value of the IMU delta velocity high frequency vibration metric (m/s)'],
-'output_obs_ang_err_peak':[float('NaN'),'Peak in-flight value of the output observer angular error (rad)'],
+'output_obs_ang_err_median':[float('NaN'),'Median in-flight value of the output observer angular error (rad)'],
-'output_obs_ang_err_mean':[float('NaN'),'Mean in-flight value of the output observer angular error (rad)'],
+'output_obs_vel_err_median':[float('NaN'),'Median in-flight value of the output observer velocity error (m/s)'],
-'output_obs_vel_err_peak':[float('NaN'),'Peak in-flight value of the output observer velocity error (m/s)'],
+'output_obs_pos_err_median':[float('NaN'),'Median in-flight value of the output observer position error (m)'],
-'output_obs_vel_err_mean':[float('NaN'),'Mean in-flight value of the output observer velocity error (m/s)'],
+'imu_dang_bias_median':[float('NaN'),'Median in-flight value of the delta angle bias vector length (rad)'],
-'output_obs_pos_err_peak':[float('NaN'),'Peak in-flight value of the output observer position error (m)'],
+'imu_dvel_bias_median':[float('NaN'),'Median in-flight value of the delta velocity bias vector length (m/s)'],
-'output_obs_pos_err_mean':[float('NaN'),'Mean in-flight value of the output observer position error (m)'],
 '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.'],
 'yaw_align_time':[float('NaN'),'The time in seconds measured from startup that the EKF completed the yaw alignment.'],
 'in_air_transition_time':[round(in_air_transition_time,1),'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.'],
@@ -1090,18 +1209,9 @@ test_results = {
 # reduction of innovation message data
 if (innov_early_end_index > (innov_late_start_index + 100)):
     # Output Observer Tracking Errors
-    temp = np.amax(ekf2_innovations['output_tracking_error[0]'][innov_late_start_index:innov_early_end_index])
+    test_results['output_obs_ang_err_median'][0] = np.median(ekf2_innovations['output_tracking_error[0]'][innov_late_start_index:innov_early_end_index])
-    if (temp > 0.0):
+    test_results['output_obs_vel_err_median'][0] = np.median(ekf2_innovations['output_tracking_error[1]'][innov_late_start_index:innov_early_end_index])
-        test_results['output_obs_ang_err_peak'][0] = temp
+    test_results['output_obs_pos_err_median'][0] = np.median(ekf2_innovations['output_tracking_error[2]'][innov_late_start_index:innov_early_end_index])
-        test_results['output_obs_ang_err_mean'][0] = np.mean(ekf2_innovations['output_tracking_error[0]'][innov_late_start_index:innov_early_end_index])
-    temp = np.amax(ekf2_innovations['output_tracking_error[1]'][innov_late_start_index:innov_early_end_index])
-    if (temp > 0.0):
-        test_results['output_obs_vel_err_peak'][0] = temp
-        test_results['output_obs_vel_err_mean'][0] = np.mean(ekf2_innovations['output_tracking_error[1]'][innov_late_start_index:innov_early_end_index])
-    temp = np.amax(ekf2_innovations['output_tracking_error[2]'][innov_late_start_index:innov_early_end_index])
-    if (temp > 0.0):
-        test_results['output_obs_pos_err_peak'][0] = temp
-        test_results['output_obs_pos_err_mean'][0] = np.mean(ekf2_innovations['output_tracking_error[2]'][innov_late_start_index:innov_early_end_index])
 
 # reduction of status message data
 if (early_end_index > (late_start_index + 100)):
@@ -1186,6 +1296,10 @@ if (early_end_index > (late_start_index + 100)):
         test_results['ofx_fail_percentage'][0] = 100.0 * (ofx_innov_fail[late_start_index:early_end_index] > 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
 
+    # IMU bias checks
+    test_results['imu_dang_bias_median'][0] = (np.median(estimator_status['states[10]'])**2 + np.median(estimator_status['states[11]'])**2 + np.median(estimator_status['states[12]'])**2)**0.5
+    test_results['imu_dvel_bias_median'][0] = (np.median(estimator_status['states[13]'])**2 + np.median(estimator_status['states[14]'])**2 + np.median(estimator_status['states[15]'])**2)**0.5
+
 # Check for internal filter nummerical faults
 test_results['filter_faults_max'][0] = np.amax(estimator_status['filter_fault_flags'])
 
@@ -1232,6 +1346,8 @@ if (test_results.get('hagl_percentage_amber')[0] > check_levels.get('hagl_amber_
 if (test_results.get('tas_percentage_amber')[0] > check_levels.get('tas_amber_warn_pct')):
     test_results['master_status'][0] = 'Warning'
     test_results['tas_sensor_status'][0] = 'Warning'
+
+# check for IMU sensor warnings
 if ((test_results.get('imu_coning_peak')[0] > check_levels.get('imu_coning_peak_warn')) or
 (test_results.get('imu_coning_mean')[0] > check_levels.get('imu_coning_mean_warn')) or
 (test_results.get('imu_hfdang_peak')[0] > check_levels.get('imu_hfdang_peak_warn')) or
@@ -1239,15 +1355,18 @@ if ((test_results.get('imu_coning_peak')[0] > check_levels.get('imu_coning_peak_
 (test_results.get('imu_hfdvel_peak')[0] > check_levels.get('imu_hfdvel_peak_warn')) or
 (test_results.get('imu_hfdvel_mean')[0] > check_levels.get('imu_hfdvel_mean_warn'))):
     test_results['master_status'][0] = 'Warning'
-    test_results['imu_sensor_status'][0] = 'Warning'
+    test_results['imu_sensor_status'][0] = 'Warning - Vibration'
-if ((test_results.get('output_obs_ang_err_peak')[0] > check_levels.get('obs_ang_err_peak_warn')) or
+
-(test_results.get('output_obs_ang_err_mean')[0] > check_levels.get('obs_ang_err_mean_warn')) or
+if ((test_results.get('imu_dang_bias_median')[0] > check_levels.get('imu_dang_bias_median_warn')) or
-(test_results.get('output_obs_vel_err_peak')[0] > check_levels.get('obs_vel_err_peak_warn')) or
+(test_results.get('imu_dvel_bias_median')[0] > check_levels.get('imu_dvel_bias_median_warn'))):
-(test_results.get('output_obs_vel_err_mean')[0] > check_levels.get('obs_vel_err_mean_warn')) or
-(test_results.get('output_obs_pos_err_peak')[0] > check_levels.get('obs_pos_err_peak_warn')) or
-(test_results.get('output_obs_pos_err_mean')[0] > check_levels.get('obs_pos_err_mean_warn'))):
     test_results['master_status'][0] = 'Warning'
-    test_results['imu_sensor_status'][0] = 'Warning'
+    test_results['imu_sensor_status'][0] = 'Warning - Bias'
+
+if ((test_results.get('output_obs_ang_err_median')[0] > check_levels.get('obs_ang_err_median_warn')) or
+(test_results.get('output_obs_vel_err_median')[0] > check_levels.get('obs_vel_err_median_warn')) or
+(test_results.get('output_obs_pos_err_median')[0] > check_levels.get('obs_pos_err_median_warn'))):
+    test_results['master_status'][0] = 'Warning'
+    test_results['imu_sensor_status'][0] = 'Warning - Output Predictor'
 
 # check for failures
 if ((test_results.get('magx_fail_percentage')[0] > check_levels.get('mag_fail_pct')) or