Tools/ecl_ekf: Improvements to ecl log analysis scripts

Fix error in scaling of population high frequency vibration metrics
Add histograms for delta angle and velocity bias data
Fix variable descriptions

Tools/ecl_ekf/batch_process_metadata_ekf.py

@@ -91,7 +91,7 @@ population_results = {
 'imu_coning_max_avg':[float('NaN'),'The mean of the maximum in-flight values of the IMU delta angle coning vibration level (mrad)'],
 'imu_coning_mean_avg':[float('NaN'),'The mean of the mean in-flight value of the IMU delta angle coning vibration level (mrad)'],
 'imu_hfdang_max_avg':[float('NaN'),'The mean of the maximum in-flight values of the IMU high frequency delta angle vibration level (mrad)'],
-'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_hfdang_mean_avg':[float('NaN'),'The mean of the mean in-flight value of the IMU delta high 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_median_avg':[float('NaN'),'The mean of the median in-flight value of the output observer angular tracking error magnitude (mrad)'],
@@ -388,9 +388,9 @@ if (len(result1) > 0 and len(result2) > 0):
 
 # IMU high frequency delta velocity vibration levels
 temp = np.asarray([population_data[k].get('imu_hfdvel_peak') for k in found_keys])
-result1 = 1000.0 * temp[np.isfinite(temp)]
+result1 = temp[np.isfinite(temp)]
 temp = np.asarray([population_data[k].get('imu_hfdvel_mean') for k in found_keys])
-result2 = 1000.0 * temp[np.isfinite(temp)]
+result2 = temp[np.isfinite(temp)]
 
 if (len(result1) > 0 and len(result2) > 0):
     population_results['imu_hfdvel_max_avg'][0] = np.mean(result1)
@@ -464,6 +464,36 @@ if (len(result) > 0):
     pp.savefig()
     plt.close(13)
 
+# IMU delta angle bias
+temp = np.asarray([population_data[k].get('imu_dang_bias_median') for k in found_keys])
+result = temp[np.isfinite(temp)]
+
+if (len(result) > 0):
+    plt.figure(14,figsize=(20,13))
+
+    plt.hist(result)
+    plt.title("Gaussian Histogram - IMU Delta Angle Bias Median")
+    plt.xlabel("imu_dang_bias_median (rad)")
+    plt.ylabel("Frequency")
+
+    pp.savefig()
+    plt.close(14)
+
+# IMU delta velocity bias
+temp = np.asarray([population_data[k].get('imu_dvel_bias_median') for k in found_keys])
+result = temp[np.isfinite(temp)]
+
+if (len(result) > 0):
+    plt.figure(15,figsize=(20,13))
+
+    plt.hist(result)
+    plt.title("Gaussian Histogram - IMU Delta Velocity Bias Median")
+    plt.xlabel("imu_dvel_bias_median (m/s)")
+    plt.ylabel("Frequency")
+
+    pp.savefig()
+    plt.close(15)
+
 # close the pdf file
 pp.close()
 print('Population summary plots saved in population_data.pdf')
@@ -525,12 +555,9 @@ single_log_results = {
 'ofx_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the optical flow sensor X-axis innovation consistency test.'],
 'ofy_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the optical flow sensor Y-axis innovation consistency test.'],
 'on_ground_transition_time':[float('NaN'),'The time in seconds measured from startup  that the EKF transitioned out of in-air mode. Set to a nan if a transition event is not detected.'],
-'output_obs_ang_err_mean':[float('NaN'),'Mean in-flight value of the output observer angular error (rad)'],
-'output_obs_ang_err_peak':[float('NaN'),'Peak in-flight value of the output observer angular error (rad)'],
-'output_obs_pos_err_mean':[float('NaN'),'Mean in-flight value of the output observer position error (m)'],
-'output_obs_pos_err_peak':[float('NaN'),'Peak 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)'],
-'output_obs_vel_err_peak':[float('NaN'),'Peak in-flight value of the output observer velocity error (m/s)'],
+'output_obs_ang_err_median':[float('NaN'),'Median in-flight value of the output observer angular error (rad)'],
+'output_obs_pos_err_median':[float('NaN'),'Median in-flight value of the output observer position error (m)'],
+'output_obs_vel_err_median':[float('NaN'),'Median in-flight value of the output observer velocity error (m/s)'],
 'pos_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the velocity sensor consolidated innovation consistency test.'],
 'pos_percentage_amber':[float('NaN'),'The percentage of in-flight position sensor consolidated innovation consistency test values > 0.5.'],
 'pos_percentage_red':[float('NaN'),'The percentage of in-flight position sensor consolidated innovation consistency test values > 1.0.'],