import numpy as np # matplotlib don't use Xwindows backend (must be before pyplot import) import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt from matplotlib.backends.backend_pdf import PdfPages def analyse_ekf(estimator_status, ekf2_innovations, sensor_preflight, check_levels, plot=False, output_plot_filename=None): if plot: # create summary plots # save the plots to PDF pp = PdfPages(output_plot_filename) # plot IMU consistency data if ('accel_inconsistency_m_s_s' in sensor_preflight.keys()) and ( 'gyro_inconsistency_rad_s' in sensor_preflight.keys()): plt.figure(0, figsize=(20, 13)) plt.subplot(2, 1, 1) plt.plot(sensor_preflight['accel_inconsistency_m_s_s'], 'b') plt.title('IMU Consistency Check Levels') plt.ylabel('acceleration (m/s/s)') plt.xlabel('data index') plt.grid() plt.subplot(2, 1, 2) plt.plot(sensor_preflight['gyro_inconsistency_rad_s'], 'b') plt.ylabel('angular rate (rad/s)') plt.xlabel('data index') pp.savefig() plt.close(0) # generate max, min and 1-std metadata innov_time = 1e-6 * ekf2_innovations['timestamp'] status_time = 1e-6 * estimator_status['timestamp'] if plot: # vertical velocity and position innovations plt.figure(1, figsize=(20, 13)) # generate metadata for velocity innovations innov_2_max_arg = np.argmax(ekf2_innovations['vel_pos_innov[2]']) innov_2_max_time = innov_time[innov_2_max_arg] innov_2_max = np.amax(ekf2_innovations['vel_pos_innov[2]']) innov_2_min_arg = np.argmin(ekf2_innovations['vel_pos_innov[2]']) innov_2_min_time = innov_time[innov_2_min_arg] innov_2_min = np.amin(ekf2_innovations['vel_pos_innov[2]']) s_innov_2_max = str(round(innov_2_max, 2)) s_innov_2_min = str(round(innov_2_min, 2)) # s_innov_2_std = str(round(np.std(ekf2_innovations['vel_pos_innov[2]']),2)) # generate metadata for position innovations innov_5_max_arg = np.argmax(ekf2_innovations['vel_pos_innov[5]']) innov_5_max_time = innov_time[innov_5_max_arg] innov_5_max = np.amax(ekf2_innovations['vel_pos_innov[5]']) innov_5_min_arg = np.argmin(ekf2_innovations['vel_pos_innov[5]']) innov_5_min_time = innov_time[innov_5_min_arg] innov_5_min = np.amin(ekf2_innovations['vel_pos_innov[5]']) s_innov_5_max = str(round(innov_5_max, 2)) s_innov_5_min = str(round(innov_5_min, 2)) # s_innov_5_std = str(round(np.std(ekf2_innovations['vel_pos_innov[5]']),2)) # generate plot for vertical velocity innovations plt.subplot(2, 1, 1) plt.plot(1e-6 * ekf2_innovations['timestamp'], ekf2_innovations['vel_pos_innov[2]'], 'b') plt.plot(1e-6 * ekf2_innovations['timestamp'], np.sqrt(ekf2_innovations['vel_pos_innov_var[2]']), 'r') plt.plot(1e-6 * ekf2_innovations['timestamp'], -np.sqrt(ekf2_innovations['vel_pos_innov_var[2]']), 'r') plt.title('Vertical Innovations') plt.ylabel('Down Vel (m/s)') plt.xlabel('time (sec)') plt.grid() plt.text(innov_2_max_time, innov_2_max, 'max=' + s_innov_2_max, fontsize=12, horizontalalignment='left', verticalalignment='bottom') plt.text(innov_2_min_time, innov_2_min, 'min=' + s_innov_2_min, fontsize=12, horizontalalignment='left', verticalalignment='top') # plt.legend(['std='+s_innov_2_std],loc='upper left',frameon=False) # generate plot for vertical position innovations plt.subplot(2, 1, 2) plt.plot(1e-6 * ekf2_innovations['timestamp'], ekf2_innovations['vel_pos_innov[5]'], 'b') plt.plot(1e-6 * ekf2_innovations['timestamp'], np.sqrt(ekf2_innovations['vel_pos_innov_var[5]']), 'r') plt.plot(1e-6 * ekf2_innovations['timestamp'], -np.sqrt(ekf2_innovations['vel_pos_innov_var[5]']), 'r') plt.ylabel('Down Pos (m)') plt.xlabel('time (sec)') plt.grid() plt.text(innov_5_max_time, innov_5_max, 'max=' + s_innov_5_max, fontsize=12, horizontalalignment='left', verticalalignment='bottom') plt.text(innov_5_min_time, innov_5_min, 'min=' + s_innov_5_min, fontsize=12, horizontalalignment='left', verticalalignment='top') # 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)) # generate North axis metadata innov_0_max_arg = np.argmax(ekf2_innovations['vel_pos_innov[0]']) innov_0_max_time = innov_time[innov_0_max_arg] innov_0_max = np.amax(ekf2_innovations['vel_pos_innov[0]']) innov_0_min_arg = np.argmin(ekf2_innovations['vel_pos_innov[0]']) innov_0_min_time = innov_time[innov_0_min_arg] innov_0_min = np.amin(ekf2_innovations['vel_pos_innov[0]']) s_innov_0_max = str(round(innov_0_max, 2)) s_innov_0_min = str(round(innov_0_min, 2)) # s_innov_0_std = str(round(np.std(ekf2_innovations['vel_pos_innov[0]']),2)) # Generate East axis metadata innov_1_max_arg = np.argmax(ekf2_innovations['vel_pos_innov[1]']) innov_1_max_time = innov_time[innov_1_max_arg] innov_1_max = np.amax(ekf2_innovations['vel_pos_innov[1]']) innov_1_min_arg = np.argmin(ekf2_innovations['vel_pos_innov[1]']) innov_1_min_time = innov_time[innov_1_min_arg] innov_1_min = np.amin(ekf2_innovations['vel_pos_innov[1]']) s_innov_1_max = str(round(innov_1_max, 2)) s_innov_1_min = str(round(innov_1_min, 2)) # s_innov_1_std = str(round(np.std(ekf2_innovations['vel_pos_innov[1]']),2)) # draw plots plt.subplot(2, 1, 1) plt.plot(1e-6 * ekf2_innovations['timestamp'], ekf2_innovations['vel_pos_innov[0]'], 'b') plt.plot(1e-6 * ekf2_innovations['timestamp'], np.sqrt(ekf2_innovations['vel_pos_innov_var[0]']), 'r') plt.plot(1e-6 * ekf2_innovations['timestamp'], -np.sqrt(ekf2_innovations['vel_pos_innov_var[0]']), 'r') plt.title('Horizontal Velocity Innovations') plt.ylabel('North Vel (m/s)') plt.xlabel('time (sec)') plt.grid() plt.text(innov_0_max_time, innov_0_max, 'max=' + s_innov_0_max, fontsize=12, horizontalalignment='left', verticalalignment='bottom') plt.text(innov_0_min_time, innov_0_min, 'min=' + s_innov_0_min, fontsize=12, horizontalalignment='left', verticalalignment='top') # plt.legend(['std='+s_innov_0_std],loc='upper left',frameon=False) plt.subplot(2, 1, 2) plt.plot(1e-6 * ekf2_innovations['timestamp'], ekf2_innovations['vel_pos_innov[1]'], 'b') plt.plot(1e-6 * ekf2_innovations['timestamp'], np.sqrt(ekf2_innovations['vel_pos_innov_var[1]']), 'r') plt.plot(1e-6 * ekf2_innovations['timestamp'], -np.sqrt(ekf2_innovations['vel_pos_innov_var[1]']), 'r') plt.ylabel('East Vel (m/s)') plt.xlabel('time (sec)') plt.grid() plt.text(innov_1_max_time, innov_1_max, 'max=' + s_innov_1_max, fontsize=12, horizontalalignment='left', verticalalignment='bottom') plt.text(innov_1_min_time, innov_1_min, 'min=' + s_innov_1_min, fontsize=12, horizontalalignment='left', verticalalignment='top') # 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)) # generate North axis metadata innov_3_max_arg = np.argmax(ekf2_innovations['vel_pos_innov[3]']) innov_3_max_time = innov_time[innov_3_max_arg] innov_3_max = np.amax(ekf2_innovations['vel_pos_innov[3]']) innov_3_min_arg = np.argmin(ekf2_innovations['vel_pos_innov[3]']) innov_3_min_time = innov_time[innov_3_min_arg] innov_3_min = np.amin(ekf2_innovations['vel_pos_innov[3]']) s_innov_3_max = str(round(innov_3_max, 2)) s_innov_3_min = str(round(innov_3_min, 2)) # s_innov_3_std = str(round(np.std(ekf2_innovations['vel_pos_innov[3]']),2)) # generate East axis metadata innov_4_max_arg = np.argmax(ekf2_innovations['vel_pos_innov[4]']) innov_4_max_time = innov_time[innov_4_max_arg] innov_4_max = np.amax(ekf2_innovations['vel_pos_innov[4]']) innov_4_min_arg = np.argmin(ekf2_innovations['vel_pos_innov[4]']) innov_4_min_time = innov_time[innov_4_min_arg] innov_4_min = np.amin(ekf2_innovations['vel_pos_innov[4]']) s_innov_4_max = str(round(innov_4_max, 2)) s_innov_4_min = str(round(innov_4_min, 2)) # s_innov_4_std = str(round(np.std(ekf2_innovations['vel_pos_innov[4]']),2)) # generate plots plt.subplot(2, 1, 1) plt.plot(1e-6 * ekf2_innovations['timestamp'], ekf2_innovations['vel_pos_innov[3]'], 'b') plt.plot(1e-6 * ekf2_innovations['timestamp'], np.sqrt(ekf2_innovations['vel_pos_innov_var[3]']), 'r') plt.plot(1e-6 * ekf2_innovations['timestamp'], -np.sqrt(ekf2_innovations['vel_pos_innov_var[3]']), 'r') plt.title('Horizontal Position Innovations') plt.ylabel('North Pos (m)') plt.xlabel('time (sec)') plt.grid() plt.text(innov_3_max_time, innov_3_max, 'max=' + s_innov_3_max, fontsize=12, horizontalalignment='left', verticalalignment='bottom') plt.text(innov_3_min_time, innov_3_min, 'min=' + s_innov_3_min, fontsize=12, horizontalalignment='left', verticalalignment='top') # plt.legend(['std='+s_innov_3_std],loc='upper left',frameon=False) plt.subplot(2, 1, 2) plt.plot(1e-6 * ekf2_innovations['timestamp'], ekf2_innovations['vel_pos_innov[4]'], 'b') plt.plot(1e-6 * ekf2_innovations['timestamp'], np.sqrt(ekf2_innovations['vel_pos_innov_var[4]']), 'r') plt.plot(1e-6 * ekf2_innovations['timestamp'], -np.sqrt(ekf2_innovations['vel_pos_innov_var[4]']), 'r') plt.ylabel('East Pos (m)') plt.xlabel('time (sec)') plt.grid() plt.text(innov_4_max_time, innov_4_max, 'max=' + s_innov_4_max, fontsize=12, horizontalalignment='left', verticalalignment='bottom') plt.text(innov_4_min_time, innov_4_min, 'min=' + s_innov_4_min, fontsize=12, horizontalalignment='left', verticalalignment='top') # 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)) # generate X axis metadata innov_0_max_arg = np.argmax(ekf2_innovations['mag_innov[0]']) innov_0_max_time = innov_time[innov_0_max_arg] innov_0_max = np.amax(ekf2_innovations['mag_innov[0]']) innov_0_min_arg = np.argmin(ekf2_innovations['mag_innov[0]']) innov_0_min_time = innov_time[innov_0_min_arg] innov_0_min = np.amin(ekf2_innovations['mag_innov[0]']) s_innov_0_max = str(round(innov_0_max, 3)) s_innov_0_min = str(round(innov_0_min, 3)) # s_innov_0_std = str(round(np.std(ekf2_innovations['mag_innov[0]']),3)) # generate Y axis metadata innov_1_max_arg = np.argmax(ekf2_innovations['mag_innov[1]']) innov_1_max_time = innov_time[innov_1_max_arg] innov_1_max = np.amax(ekf2_innovations['mag_innov[1]']) innov_1_min_arg = np.argmin(ekf2_innovations['mag_innov[1]']) innov_1_min_time = innov_time[innov_1_min_arg] innov_1_min = np.amin(ekf2_innovations['mag_innov[1]']) s_innov_1_max = str(round(innov_1_max, 3)) s_innov_1_min = str(round(innov_1_min, 3)) # s_innov_1_std = str(round(np.std(ekf2_innovations['mag_innov[1]']),3)) # generate Z axis metadata innov_2_max_arg = np.argmax(ekf2_innovations['mag_innov[2]']) innov_2_max_time = innov_time[innov_2_max_arg] innov_2_max = np.amax(ekf2_innovations['mag_innov[2]']) innov_2_min_arg = np.argmin(ekf2_innovations['mag_innov[2]']) innov_2_min_time = innov_time[innov_2_min_arg] innov_2_min = np.amin(ekf2_innovations['mag_innov[2]']) s_innov_2_max = str(round(innov_2_max, 3)) s_innov_2_min = str(round(innov_2_min, 3)) # s_innov_2_std = str(round(np.std(ekf2_innovations['mag_innov[0]']),3)) # draw plots plt.subplot(3, 1, 1) plt.plot(1e-6 * ekf2_innovations['timestamp'], ekf2_innovations['mag_innov[0]'], 'b') plt.plot(1e-6 * ekf2_innovations['timestamp'], np.sqrt(ekf2_innovations['mag_innov_var[0]']), 'r') plt.plot(1e-6 * ekf2_innovations['timestamp'], -np.sqrt(ekf2_innovations['mag_innov_var[0]']), 'r') plt.title('Magnetometer Innovations') plt.ylabel('X (Gauss)') plt.xlabel('time (sec)') plt.grid() plt.text(innov_0_max_time, innov_0_max, 'max=' + s_innov_0_max, fontsize=12, horizontalalignment='left', verticalalignment='bottom') plt.text(innov_0_min_time, innov_0_min, 'min=' + s_innov_0_min, fontsize=12, horizontalalignment='left', verticalalignment='top') # plt.legend(['std='+s_innov_0_std],loc='upper left',frameon=False) plt.subplot(3, 1, 2) plt.plot(1e-6 * ekf2_innovations['timestamp'], ekf2_innovations['mag_innov[1]'], 'b') plt.plot(1e-6 * ekf2_innovations['timestamp'], np.sqrt(ekf2_innovations['mag_innov_var[1]']), 'r') plt.plot(1e-6 * ekf2_innovations['timestamp'], -np.sqrt(ekf2_innovations['mag_innov_var[1]']), 'r') plt.ylabel('Y (Gauss)') plt.xlabel('time (sec)') plt.grid() plt.text(innov_1_max_time, innov_1_max, 'max=' + s_innov_1_max, fontsize=12, horizontalalignment='left', verticalalignment='bottom') plt.text(innov_1_min_time, innov_1_min, 'min=' + s_innov_1_min, fontsize=12, horizontalalignment='left', verticalalignment='top') # plt.legend(['std='+s_innov_1_std],loc='upper left',frameon=False) plt.subplot(3, 1, 3) plt.plot(1e-6 * ekf2_innovations['timestamp'], ekf2_innovations['mag_innov[2]'], 'b') plt.plot(1e-6 * ekf2_innovations['timestamp'], np.sqrt(ekf2_innovations['mag_innov_var[2]']), 'r') plt.plot(1e-6 * ekf2_innovations['timestamp'], -np.sqrt(ekf2_innovations['mag_innov_var[2]']), 'r') plt.ylabel('Z (Gauss)') plt.xlabel('time (sec)') plt.grid() plt.text(innov_2_max_time, innov_2_max, 'max=' + s_innov_2_max, fontsize=12, horizontalalignment='left', verticalalignment='bottom') plt.text(innov_2_min_time, innov_2_min, 'min=' + s_innov_2_min, fontsize=12, horizontalalignment='left', verticalalignment='top') # 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)) # generate metadata innov_0_max_arg = np.argmax(ekf2_innovations['heading_innov']) innov_0_max_time = innov_time[innov_0_max_arg] innov_0_max = np.amax(ekf2_innovations['heading_innov']) innov_0_min_arg = np.argmin(ekf2_innovations['heading_innov']) innov_0_min_time = innov_time[innov_0_min_arg] innov_0_min = np.amin(ekf2_innovations['heading_innov']) s_innov_0_max = str(round(innov_0_max, 3)) s_innov_0_min = str(round(innov_0_min, 3)) # s_innov_0_std = str(round(np.std(ekf2_innovations['heading_innov']),3)) # draw plot plt.plot(1e-6 * ekf2_innovations['timestamp'], ekf2_innovations['heading_innov'], 'b') plt.plot(1e-6 * ekf2_innovations['timestamp'], np.sqrt(ekf2_innovations['heading_innov_var']), 'r') plt.plot(1e-6 * ekf2_innovations['timestamp'], -np.sqrt(ekf2_innovations['heading_innov_var']), 'r') plt.title('Magnetic Heading Innovations') plt.ylabel('Heaing (rad)') plt.xlabel('time (sec)') plt.grid() plt.text(innov_0_max_time, innov_0_max, 'max=' + s_innov_0_max, fontsize=12, horizontalalignment='left', verticalalignment='bottom') plt.text(innov_0_min_time, innov_0_min, 'min=' + s_innov_0_min, fontsize=12, horizontalalignment='left', verticalalignment='top') # 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)) # generate airspeed metadata airspeed_innov_max_arg = np.argmax(ekf2_innovations['airspeed_innov']) airspeed_innov_max_time = innov_time[airspeed_innov_max_arg] airspeed_innov_max = np.amax(ekf2_innovations['airspeed_innov']) airspeed_innov_min_arg = np.argmin(ekf2_innovations['airspeed_innov']) airspeed_innov_min_time = innov_time[airspeed_innov_min_arg] airspeed_innov_min = np.amin(ekf2_innovations['airspeed_innov']) s_airspeed_innov_max = str(round(airspeed_innov_max, 3)) s_airspeed_innov_min = str(round(airspeed_innov_min, 3)) # generate sideslip metadata beta_innov_max_arg = np.argmax(ekf2_innovations['beta_innov']) beta_innov_max_time = innov_time[beta_innov_max_arg] beta_innov_max = np.amax(ekf2_innovations['beta_innov']) beta_innov_min_arg = np.argmin(ekf2_innovations['beta_innov']) beta_innov_min_time = innov_time[beta_innov_min_arg] beta_innov_min = np.amin(ekf2_innovations['beta_innov']) s_beta_innov_max = str(round(beta_innov_max, 3)) s_beta_innov_min = str(round(beta_innov_min, 3)) # draw plots plt.subplot(2, 1, 1) plt.plot(1e-6 * ekf2_innovations['timestamp'], ekf2_innovations['airspeed_innov'], 'b') plt.plot(1e-6 * ekf2_innovations['timestamp'], np.sqrt(ekf2_innovations['airspeed_innov_var']), 'r') plt.plot(1e-6 * ekf2_innovations['timestamp'], -np.sqrt(ekf2_innovations['airspeed_innov_var']), 'r') plt.title('True Airspeed Innovations') plt.ylabel('innovation (m/sec)') plt.xlabel('time (sec)') plt.grid() plt.text(airspeed_innov_max_time, airspeed_innov_max, 'max=' + s_airspeed_innov_max, fontsize=12, horizontalalignment='left', verticalalignment='bottom') plt.text(airspeed_innov_min_time, airspeed_innov_min, 'min=' + s_airspeed_innov_min, fontsize=12, horizontalalignment='left', verticalalignment='top') plt.subplot(2, 1, 2) plt.plot(1e-6 * ekf2_innovations['timestamp'], ekf2_innovations['beta_innov'], 'b') plt.plot(1e-6 * ekf2_innovations['timestamp'], np.sqrt(ekf2_innovations['beta_innov_var']), 'r') plt.plot(1e-6 * ekf2_innovations['timestamp'], -np.sqrt(ekf2_innovations['beta_innov_var']), 'r') plt.title('Sythetic Sideslip Innovations') plt.ylabel('innovation (rad)') plt.xlabel('time (sec)') plt.grid() plt.text(beta_innov_max_time, beta_innov_max, 'max=' + s_beta_innov_max, fontsize=12, horizontalalignment='left', verticalalignment='bottom') 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)) # generate X axis metadata flow_innov_x_max_arg = np.argmax(ekf2_innovations['flow_innov[0]']) flow_innov_x_max_time = innov_time[flow_innov_x_max_arg] flow_innov_x_max = np.amax(ekf2_innovations['flow_innov[0]']) flow_innov_x_min_arg = np.argmin(ekf2_innovations['flow_innov[0]']) flow_innov_x_min_time = innov_time[flow_innov_x_min_arg] flow_innov_x_min = np.amin(ekf2_innovations['flow_innov[0]']) s_flow_innov_x_max = str(round(flow_innov_x_max, 3)) s_flow_innov_x_min = str(round(flow_innov_x_min, 3)) # s_flow_innov_x_std = str(round(np.std(ekf2_innovations['flow_innov[0]']),3)) # generate Y axis metadata flow_innov_y_max_arg = np.argmax(ekf2_innovations['flow_innov[1]']) flow_innov_y_max_time = innov_time[flow_innov_y_max_arg] flow_innov_y_max = np.amax(ekf2_innovations['flow_innov[1]']) flow_innov_y_min_arg = np.argmin(ekf2_innovations['flow_innov[1]']) flow_innov_y_min_time = innov_time[flow_innov_y_min_arg] flow_innov_y_min = np.amin(ekf2_innovations['flow_innov[1]']) s_flow_innov_y_max = str(round(flow_innov_y_max, 3)) s_flow_innov_y_min = str(round(flow_innov_y_min, 3)) # s_flow_innov_y_std = str(round(np.std(ekf2_innovations['flow_innov[1]']),3)) # draw plots plt.subplot(2, 1, 1) plt.plot(1e-6 * ekf2_innovations['timestamp'], ekf2_innovations['flow_innov[0]'], 'b') plt.plot(1e-6 * ekf2_innovations['timestamp'], np.sqrt(ekf2_innovations['flow_innov_var[0]']), 'r') plt.plot(1e-6 * ekf2_innovations['timestamp'], -np.sqrt(ekf2_innovations['flow_innov_var[0]']), 'r') plt.title('Optical Flow Innovations') plt.ylabel('X (rad/sec)') plt.xlabel('time (sec)') plt.grid() plt.text(flow_innov_x_max_time, flow_innov_x_max, 'max=' + s_flow_innov_x_max, fontsize=12, horizontalalignment='left', verticalalignment='bottom') plt.text(flow_innov_x_min_time, flow_innov_x_min, 'min=' + s_flow_innov_x_min, fontsize=12, horizontalalignment='left', verticalalignment='top') # plt.legend(['std='+s_flow_innov_x_std],loc='upper left',frameon=False) plt.subplot(2, 1, 2) plt.plot(1e-6 * ekf2_innovations['timestamp'], ekf2_innovations['flow_innov[1]'], 'b') plt.plot(1e-6 * ekf2_innovations['timestamp'], np.sqrt(ekf2_innovations['flow_innov_var[1]']), 'r') plt.plot(1e-6 * ekf2_innovations['timestamp'], -np.sqrt(ekf2_innovations['flow_innov_var[1]']), 'r') plt.ylabel('Y (rad/sec)') plt.xlabel('time (sec)') plt.grid() plt.text(flow_innov_y_max_time, flow_innov_y_max, 'max=' + s_flow_innov_y_max, fontsize=12, horizontalalignment='left', verticalalignment='bottom') plt.text(flow_innov_y_min_time, flow_innov_y_min, 'min=' + s_flow_innov_y_min, fontsize=12, horizontalalignment='left', verticalalignment='top') # 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 # magnetometer data mag_test_max_arg = np.argmax(estimator_status['mag_test_ratio']) mag_test_max_time = status_time[mag_test_max_arg] mag_test_max = np.amax(estimator_status['mag_test_ratio']) mag_test_mean = np.mean(estimator_status['mag_test_ratio']) # velocity data (GPS) vel_test_max_arg = np.argmax(estimator_status['vel_test_ratio']) vel_test_max_time = status_time[vel_test_max_arg] vel_test_max = np.amax(estimator_status['vel_test_ratio']) vel_test_mean = np.mean(estimator_status['vel_test_ratio']) # horizontal position data (GPS or external vision) pos_test_max_arg = np.argmax(estimator_status['pos_test_ratio']) pos_test_max_time = status_time[pos_test_max_arg] pos_test_max = np.amax(estimator_status['pos_test_ratio']) pos_test_mean = np.mean(estimator_status['pos_test_ratio']) # height data (Barometer, GPS or rangefinder) hgt_test_max_arg = np.argmax(estimator_status['hgt_test_ratio']) hgt_test_max_time = status_time[hgt_test_max_arg] hgt_test_max = np.amax(estimator_status['hgt_test_ratio']) hgt_test_mean = np.mean(estimator_status['hgt_test_ratio']) # airspeed data tas_test_max_arg = np.argmax(estimator_status['tas_test_ratio']) tas_test_max_time = status_time[tas_test_max_arg] tas_test_max = np.amax(estimator_status['tas_test_ratio']) tas_test_mean = np.mean(estimator_status['tas_test_ratio']) # height above ground data (rangefinder) hagl_test_max_arg = np.argmax(estimator_status['hagl_test_ratio']) hagl_test_max_time = status_time[hagl_test_max_arg] hagl_test_max = np.amax(estimator_status['hagl_test_ratio']) hagl_test_mean = np.mean(estimator_status['hagl_test_ratio']) if plot: # plot normalised innovation test levels plt.figure(8, figsize=(20, 13)) if tas_test_max == 0.0: n_plots = 3 else: n_plots = 4 plt.subplot(n_plots, 1, 1) plt.plot(status_time, estimator_status['mag_test_ratio'], 'b') plt.title('Normalised Innovation Test Levels') plt.ylabel('mag') plt.xlabel('time (sec)') plt.grid() plt.text(mag_test_max_time, mag_test_max, 'max=' + str(round(mag_test_max, 2)) + ' , mean=' + str(round(mag_test_mean, 2)), fontsize=12, horizontalalignment='left', verticalalignment='bottom', color='b') plt.subplot(n_plots, 1, 2) plt.plot(status_time, estimator_status['vel_test_ratio'], 'b') plt.plot(status_time, estimator_status['pos_test_ratio'], 'r') plt.ylabel('vel,pos') plt.xlabel('time (sec)') plt.grid() plt.text(vel_test_max_time, vel_test_max, 'vel max=' + str(round(vel_test_max, 2)) + ' , mean=' + str(round(vel_test_mean, 2)), fontsize=12, horizontalalignment='left', verticalalignment='bottom', color='b') plt.text(pos_test_max_time, pos_test_max, 'pos max=' + str(round(pos_test_max, 2)) + ' , mean=' + str(round(pos_test_mean, 2)), fontsize=12, horizontalalignment='left', verticalalignment='bottom', color='r') plt.subplot(n_plots, 1, 3) plt.plot(status_time, estimator_status['hgt_test_ratio'], 'b') plt.ylabel('hgt') plt.xlabel('time (sec)') plt.grid() plt.text(hgt_test_max_time, hgt_test_max, 'hgt max=' + str(round(hgt_test_max, 2)) + ' , mean=' + str(round(hgt_test_mean, 2)), fontsize=12, horizontalalignment='left', verticalalignment='bottom', color='b') if hagl_test_max > 0.0: plt.plot(status_time, estimator_status['hagl_test_ratio'], 'r') plt.text(hagl_test_max_time, hagl_test_max, 'hagl max=' + str(round(hagl_test_max, 2)) + ' , mean=' + str(round(hagl_test_mean, 2)), fontsize=12, horizontalalignment='left', verticalalignment='bottom', color='r') plt.ylabel('hgt,HAGL') if n_plots == 4: plt.subplot(n_plots, 1, 4) plt.plot(status_time, estimator_status['tas_test_ratio'], 'b') plt.ylabel('TAS') plt.xlabel('time (sec)') plt.grid() 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 # 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 tilt_aligned = ((2 ** 0 & estimator_status['control_mode_flags']) > 0) * 1 yaw_aligned = ((2 ** 1 & estimator_status['control_mode_flags']) > 0) * 1 using_gps = ((2 ** 2 & estimator_status['control_mode_flags']) > 0) * 1 using_optflow = ((2 ** 3 & estimator_status['control_mode_flags']) > 0) * 1 using_magyaw = ((2 ** 4 & estimator_status['control_mode_flags']) > 0) * 1 using_mag3d = ((2 ** 5 & estimator_status['control_mode_flags']) > 0) * 1 using_magdecl = ((2 ** 6 & estimator_status['control_mode_flags']) > 0) * 1 airborne = ((2 ** 7 & estimator_status['control_mode_flags']) > 0) * 1 estimating_wind = ((2 ** 8 & estimator_status['control_mode_flags']) > 0) * 1 using_barohgt = ((2 ** 9 & estimator_status['control_mode_flags']) > 0) * 1 using_rnghgt = ((2 ** 10 & estimator_status['control_mode_flags']) > 0) * 1 using_gpshgt = ((2 ** 11 & estimator_status['control_mode_flags']) > 0) * 1 using_evpos = ((2 ** 12 & estimator_status['control_mode_flags']) > 0) * 1 using_evyaw = ((2 ** 13 & estimator_status['control_mode_flags']) > 0) * 1 using_evhgt = ((2 ** 14 & estimator_status['control_mode_flags']) > 0) * 1 # calculate in-air transition time if (np.amin(airborne) < 0.5) and (np.amax(airborne) > 0.5): in_air_transtion_time_arg = np.argmax(np.diff(airborne)) in_air_transition_time = status_time[in_air_transtion_time_arg] elif (np.amax(airborne) > 0.5): in_air_transition_time = np.amin(status_time) print('log starts while in-air at ' + str(round(in_air_transition_time, 1)) + ' sec') else: in_air_transition_time = float('NaN') print('always on ground') # calculate on-ground transition time if (np.amin(np.diff(airborne)) < 0.0): on_ground_transition_time_arg = np.argmin(np.diff(airborne)) on_ground_transition_time = status_time[on_ground_transition_time_arg] elif (np.amax(airborne) > 0.5): on_ground_transition_time = np.amax(status_time) print('log finishes while in-air at ' + str(round(on_ground_transition_time, 1)) + ' sec') else: on_ground_transition_time = float('NaN') print('always on ground') if (np.amax(np.diff(airborne)) > 0.5) and (np.amin(np.diff(airborne)) < -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: in_air_duration = float('NaN') else: in_air_duration = float('NaN') # calculate alignment completion times tilt_align_time_arg = np.argmax(np.diff(tilt_aligned)) tilt_align_time = status_time[tilt_align_time_arg] yaw_align_time_arg = np.argmax(np.diff(yaw_aligned)) yaw_align_time = status_time[yaw_align_time_arg] # calculate position aiding start times gps_aid_time_arg = np.argmax(np.diff(using_gps)) gps_aid_time = status_time[gps_aid_time_arg] optflow_aid_time_arg = np.argmax(np.diff(using_optflow)) optflow_aid_time = status_time[optflow_aid_time_arg] evpos_aid_time_arg = np.argmax(np.diff(using_evpos)) evpos_aid_time = status_time[evpos_aid_time_arg] # calculate height aiding start times barohgt_aid_time_arg = np.argmax(np.diff(using_barohgt)) barohgt_aid_time = status_time[barohgt_aid_time_arg] gpshgt_aid_time_arg = np.argmax(np.diff(using_gpshgt)) gpshgt_aid_time = status_time[gpshgt_aid_time_arg] rnghgt_aid_time_arg = np.argmax(np.diff(using_rnghgt)) rnghgt_aid_time = status_time[rnghgt_aid_time_arg] evhgt_aid_time_arg = np.argmax(np.diff(using_evhgt)) evhgt_aid_time = status_time[evhgt_aid_time_arg] # calculate magnetometer aiding start times using_magyaw_time_arg = np.argmax(np.diff(using_magyaw)) using_magyaw_time = status_time[using_magyaw_time_arg] using_mag3d_time_arg = np.argmax(np.diff(using_mag3d)) using_mag3d_time = status_time[using_mag3d_time_arg] using_magdecl_time_arg = np.argmax(np.diff(using_magdecl)) using_magdecl_time = status_time[using_magdecl_time_arg] if plot: # control mode summary plot A plt.figure(9, figsize=(20, 13)) # subplot for alignment completion plt.subplot(4, 1, 1) plt.title('EKF Control Status - Figure A') plt.plot(status_time, tilt_aligned, 'b') plt.plot(status_time, yaw_aligned, 'r') plt.ylim(-0.1, 1.1) plt.ylabel('aligned') plt.grid() if np.amin(tilt_aligned) > 0: plt.text(tilt_align_time, 0.5, 'no pre-arm data - cannot calculate alignment completion times', fontsize=12, horizontalalignment='left', verticalalignment='center', color='black') else: plt.text(tilt_align_time, 0.33, 'tilt alignment at ' + str(round(tilt_align_time, 1)) + ' sec', fontsize=12, horizontalalignment='left', verticalalignment='center', color='b') plt.text(yaw_align_time, 0.67, 'yaw alignment at ' + str(round(tilt_align_time, 1)) + ' sec', fontsize=12, horizontalalignment='left', verticalalignment='center', color='r') # subplot for position aiding plt.subplot(4, 1, 2) plt.plot(status_time, using_gps, 'b') plt.plot(status_time, using_optflow, 'r') plt.plot(status_time, using_evpos, 'g') plt.ylim(-0.1, 1.1) plt.ylabel('pos aiding') plt.grid() if np.amin(using_gps) > 0: plt.text(gps_aid_time, 0.25, 'no pre-arm data - cannot calculate GPS aiding start time', fontsize=12, horizontalalignment='left', verticalalignment='center', color='b') elif np.amax(using_gps) > 0: plt.text(gps_aid_time, 0.25, 'GPS aiding at ' + str(round(gps_aid_time, 1)) + ' sec', fontsize=12, horizontalalignment='left', verticalalignment='center', color='b') if np.amin(using_optflow) > 0: plt.text(optflow_aid_time, 0.50, 'no pre-arm data - cannot calculate optical flow aiding start time', fontsize=12, horizontalalignment='left', verticalalignment='center', color='r') elif np.amax(using_optflow) > 0: plt.text(optflow_aid_time, 0.50, 'optical flow aiding at ' + str(round(optflow_aid_time, 1)) + ' sec', fontsize=12, horizontalalignment='left', verticalalignment='center', color='r') if np.amin(using_evpos) > 0: plt.text(evpos_aid_time, 0.75, 'no pre-arm data - cannot calculate external vision aiding start time', fontsize=12, horizontalalignment='left', verticalalignment='center', color='g') elif np.amax(using_evpos) > 0: plt.text(evpos_aid_time, 0.75, 'external vision aiding at ' + str(round(evpos_aid_time, 1)) + ' sec', fontsize=12, horizontalalignment='left', verticalalignment='center', color='g') # subplot for height aiding plt.subplot(4, 1, 3) plt.plot(status_time, using_barohgt, 'b') plt.plot(status_time, using_gpshgt, 'r') plt.plot(status_time, using_rnghgt, 'g') plt.plot(status_time, using_evhgt, 'c') plt.ylim(-0.1, 1.1) plt.ylabel('hgt aiding') plt.grid() if np.amin(using_barohgt) > 0: plt.text(barohgt_aid_time, 0.2, 'no pre-arm data - cannot calculate Baro aiding start time', fontsize=12, horizontalalignment='left', verticalalignment='center', color='b') elif np.amax(using_barohgt) > 0: plt.text(barohgt_aid_time, 0.2, 'Baro aiding at ' + str(round(gps_aid_time, 1)) + ' sec', fontsize=12, horizontalalignment='left', verticalalignment='center', color='b') if np.amin(using_gpshgt) > 0: plt.text(gpshgt_aid_time, 0.4, 'no pre-arm data - cannot calculate GPS aiding start time', fontsize=12, horizontalalignment='left', verticalalignment='center', color='r') elif np.amax(using_gpshgt) > 0: plt.text(gpshgt_aid_time, 0.4, 'GPS aiding at ' + str(round(gpshgt_aid_time, 1)) + ' sec', fontsize=12, horizontalalignment='left', verticalalignment='center', color='r') if np.amin(using_rnghgt) > 0: plt.text(rnghgt_aid_time, 0.6, 'no pre-arm data - cannot calculate rangfinder aiding start time', fontsize=12, horizontalalignment='left', verticalalignment='center', color='g') elif np.amax(using_rnghgt) > 0: plt.text(rnghgt_aid_time, 0.6, 'rangefinder aiding at ' + str(round(rnghgt_aid_time, 1)) + ' sec', fontsize=12, horizontalalignment='left', verticalalignment='center', color='g') if np.amin(using_evhgt) > 0: plt.text(evhgt_aid_time, 0.8, 'no pre-arm data - cannot calculate external vision aiding start time', fontsize=12, horizontalalignment='left', verticalalignment='center', color='c') elif np.amax(using_evhgt) > 0: plt.text(evhgt_aid_time, 0.8, 'external vision aiding at ' + str(round(evhgt_aid_time, 1)) + ' sec', fontsize=12, horizontalalignment='left', verticalalignment='center', color='c') # subplot for magnetometer aiding plt.subplot(4, 1, 4) plt.plot(status_time, using_magyaw, 'b') plt.plot(status_time, using_mag3d, 'r') plt.plot(status_time, using_magdecl, 'g') plt.ylim(-0.1, 1.1) plt.ylabel('mag aiding') plt.xlabel('time (sec)') plt.grid() if np.amin(using_magyaw) > 0: plt.text(using_magyaw_time, 0.25, 'no pre-arm data - cannot calculate magnetic yaw aiding start time', fontsize=12, horizontalalignment='left', verticalalignment='center', color='b') elif np.amax(using_magyaw) > 0: plt.text(using_magyaw_time, 0.25, 'magnetic yaw aiding at ' + str(round(using_magyaw_time, 1)) + ' sec', fontsize=12, horizontalalignment='right', verticalalignment='center', color='b') if np.amin(using_mag3d) > 0: plt.text(using_mag3d_time, 0.50, 'no pre-arm data - cannot calculate 3D magnetoemter aiding start time', fontsize=12, horizontalalignment='left', verticalalignment='center', color='r') elif np.amax(using_mag3d) > 0: plt.text(using_mag3d_time, 0.50, 'magnetometer 3D aiding at ' + str(round(using_mag3d_time, 1)) + ' sec', fontsize=12, horizontalalignment='left', verticalalignment='center', color='r') if np.amin(using_magdecl) > 0: plt.text(using_magdecl_time, 0.75, 'no pre-arm data - cannot magnetic declination aiding start time', fontsize=12, horizontalalignment='left', verticalalignment='center', color='g') 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)) # subplot for airborne status plt.subplot(2, 1, 1) plt.title('EKF Control Status - Figure B') plt.plot(status_time, airborne, 'b') plt.ylim(-0.1, 1.1) plt.ylabel('airborne') plt.grid() if np.amax(np.diff(airborne)) < 0.5: plt.text(in_air_transition_time, 0.67, 'ground to air transition not detected', fontsize=12, horizontalalignment='left', verticalalignment='center', color='b') else: plt.text(in_air_transition_time, 0.67, 'in-air at ' + str(round(in_air_transition_time, 1)) + ' sec', fontsize=12, horizontalalignment='left', verticalalignment='center', color='b') if np.amin(np.diff(airborne)) > -0.5: plt.text(on_ground_transition_time, 0.33, 'air to ground transition not detected', fontsize=12, horizontalalignment='left', verticalalignment='center', color='b') else: plt.text(on_ground_transition_time, 0.33, 'on-ground at ' + str(round(on_ground_transition_time, 1)) + ' sec', fontsize=12, horizontalalignment='right', verticalalignment='center', color='b') if in_air_duration > 0.0: plt.text((in_air_transition_time + on_ground_transition_time) / 2, 0.5, 'duration = ' + str(round(in_air_duration, 1)) + ' sec', fontsize=12, horizontalalignment='center', verticalalignment='center', color='b') # subplot for wind estimation status plt.subplot(2, 1, 2) plt.plot(status_time, estimating_wind, 'b') plt.ylim(-0.1, 1.1) plt.ylabel('estimating wind') plt.xlabel('time (sec)') plt.grid() pp.savefig() plt.close(10) # 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 the height above ground observation has been rejected # 9 - true if the X optical flow observation has been rejected # 10 - true if the Y optical flow observation has been rejected vel_innov_fail = ((2 ** 0 & estimator_status['innovation_check_flags']) > 0) * 1 posh_innov_fail = ((2 ** 1 & estimator_status['innovation_check_flags']) > 0) * 1 posv_innov_fail = ((2 ** 2 & estimator_status['innovation_check_flags']) > 0) * 1 magx_innov_fail = ((2 ** 3 & estimator_status['innovation_check_flags']) > 0) * 1 magy_innov_fail = ((2 ** 4 & estimator_status['innovation_check_flags']) > 0) * 1 magz_innov_fail = ((2 ** 5 & estimator_status['innovation_check_flags']) > 0) * 1 yaw_innov_fail = ((2 ** 6 & estimator_status['innovation_check_flags']) > 0) * 1 tas_innov_fail = ((2 ** 7 & estimator_status['innovation_check_flags']) > 0) * 1 hagl_innov_fail = ((2 ** 8 & estimator_status['innovation_check_flags']) > 0) * 1 ofx_innov_fail = ((2 ** 9 & estimator_status['innovation_check_flags']) > 0) * 1 ofy_innov_fail = ((2 ** 10 & estimator_status['innovation_check_flags']) > 0) * 1 if plot: # plot innovation_check_flags summary plt.figure(11, figsize=(20, 13)) plt.subplot(5, 1, 1) plt.title('EKF Innovation Test Fails') plt.plot(status_time, vel_innov_fail, 'b', label='vel NED') plt.plot(status_time, posh_innov_fail, 'r', label='pos NE') plt.ylim(-0.1, 1.1) plt.ylabel('failed') plt.legend(loc='upper left') plt.grid() plt.subplot(5, 1, 2) plt.plot(status_time, posv_innov_fail, 'b', label='hgt absolute') plt.plot(status_time, hagl_innov_fail, 'r', label='hgt above ground') plt.ylim(-0.1, 1.1) plt.ylabel('failed') plt.legend(loc='upper left') plt.grid() plt.subplot(5, 1, 3) plt.plot(status_time, magx_innov_fail, 'b', label='mag_x') plt.plot(status_time, magy_innov_fail, 'r', label='mag_y') plt.plot(status_time, magz_innov_fail, 'g', label='mag_z') plt.plot(status_time, yaw_innov_fail, 'c', label='yaw') plt.legend(loc='upper left') plt.ylim(-0.1, 1.1) plt.ylabel('failed') plt.grid() plt.subplot(5, 1, 4) plt.plot(status_time, tas_innov_fail, 'b', label='airspeed') plt.ylim(-0.1, 1.1) plt.ylabel('failed') plt.legend(loc='upper left') plt.grid() plt.subplot(5, 1, 5) plt.plot(status_time, ofx_innov_fail, 'b', label='flow X') plt.plot(status_time, ofy_innov_fail, 'r', label='flow Y') plt.ylim(-0.1, 1.1) plt.ylabel('failed') plt.xlabel('time (sec') plt.legend(loc='upper left') plt.grid() pp.savefig() plt.close(11) # gps_check_fail_flags summary plt.figure(12, figsize=(20, 13)) # 0 : minimum required sat count fail # 1 : minimum required GDoP fail # 2 : maximum allowed horizontal position error fail # 3 : maximum allowed vertical position error fail # 4 : maximum allowed speed error fail # 5 : maximum allowed horizontal position drift fail # 6 : maximum allowed vertical position drift fail # 7 : maximum allowed horizontal speed fail # 8 : maximum allowed vertical velocity discrepancy fail nsat_fail = ((2 ** 0 & estimator_status['gps_check_fail_flags']) > 0) * 1 gdop_fail = ((2 ** 1 & estimator_status['gps_check_fail_flags']) > 0) * 1 herr_fail = ((2 ** 2 & estimator_status['gps_check_fail_flags']) > 0) * 1 verr_fail = ((2 ** 3 & estimator_status['gps_check_fail_flags']) > 0) * 1 serr_fail = ((2 ** 4 & estimator_status['gps_check_fail_flags']) > 0) * 1 hdrift_fail = ((2 ** 5 & estimator_status['gps_check_fail_flags']) > 0) * 1 vdrift_fail = ((2 ** 6 & estimator_status['gps_check_fail_flags']) > 0) * 1 hspd_fail = ((2 ** 7 & estimator_status['gps_check_fail_flags']) > 0) * 1 veld_diff_fail = ((2 ** 8 & estimator_status['gps_check_fail_flags']) > 0) * 1 plt.subplot(2, 1, 1) plt.title('GPS Direct Output Check Failures') plt.plot(status_time, nsat_fail, 'b', label='N sats') plt.plot(status_time, gdop_fail, 'r', label='GDOP') plt.plot(status_time, herr_fail, 'g', label='horiz pos error') plt.plot(status_time, verr_fail, 'c', label='vert pos error') plt.plot(status_time, serr_fail, 'm', label='speed error') plt.ylim(-0.1, 1.1) plt.ylabel('failed') plt.legend(loc='upper right') plt.grid() plt.subplot(2, 1, 2) plt.title('GPS Derived Output Check Failures') plt.plot(status_time, hdrift_fail, 'b', label='horiz drift') plt.plot(status_time, vdrift_fail, 'r', label='vert drift') plt.plot(status_time, hspd_fail, 'g', label='horiz speed') plt.plot(status_time, veld_diff_fail, 'c', label='vert vel inconsistent') plt.ylim(-0.1, 1.1) plt.ylabel('failed') plt.xlabel('time (sec') plt.legend(loc='upper right') plt.grid() pp.savefig() plt.close(12) # filter reported accuracy plt.figure(13, figsize=(20, 13)) plt.title('Reported Accuracy') plt.plot(status_time, estimator_status['pos_horiz_accuracy'], 'b', label='horizontal') plt.plot(status_time, estimator_status['pos_vert_accuracy'], 'r', label='vertical') plt.ylabel('accuracy (m)') plt.xlabel('time (sec') plt.legend(loc='upper right') plt.grid() pp.savefig() plt.close(13) # Plot the EKF IMU vibration metrics plt.figure(14, figsize=(20, 13)) vibe_coning_max_arg = np.argmax(estimator_status['vibe[0]']) vibe_coning_max_time = status_time[vibe_coning_max_arg] vibe_coning_max = np.amax(estimator_status['vibe[0]']) vibe_hf_dang_max_arg = np.argmax(estimator_status['vibe[1]']) vibe_hf_dang_max_time = status_time[vibe_hf_dang_max_arg] vibe_hf_dang_max = np.amax(estimator_status['vibe[1]']) vibe_hf_dvel_max_arg = np.argmax(estimator_status['vibe[2]']) vibe_hf_dvel_max_time = status_time[vibe_hf_dvel_max_arg] vibe_hf_dvel_max = np.amax(estimator_status['vibe[2]']) plt.subplot(3, 1, 1) plt.plot(1e-6 * estimator_status['timestamp'], 1000.0 * estimator_status['vibe[0]'], 'b') plt.title('IMU Vibration Metrics') plt.ylabel('Del Ang Coning (mrad)') plt.grid() plt.text(vibe_coning_max_time, 1000.0 * vibe_coning_max, 'max=' + str(round(1000.0 * vibe_coning_max, 5)), fontsize=12, horizontalalignment='left', verticalalignment='top') plt.subplot(3, 1, 2) plt.plot(1e-6 * estimator_status['timestamp'], 1000.0 * estimator_status['vibe[1]'], 'b') plt.ylabel('HF Del Ang (mrad)') plt.grid() plt.text(vibe_hf_dang_max_time, 1000.0 * vibe_hf_dang_max, 'max=' + str(round(1000.0 * vibe_hf_dang_max, 3)), fontsize=12, horizontalalignment='left', verticalalignment='top') plt.subplot(3, 1, 3) plt.plot(1e-6 * estimator_status['timestamp'], estimator_status['vibe[2]'], 'b') plt.ylabel('HF Del Vel (m/s)') plt.xlabel('time (sec)') 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)) ang_track_err_max_arg = np.argmax(ekf2_innovations['output_tracking_error[0]']) ang_track_err_max_time = innov_time[ang_track_err_max_arg] ang_track_err_max = np.amax(ekf2_innovations['output_tracking_error[0]']) vel_track_err_max_arg = np.argmax(ekf2_innovations['output_tracking_error[1]']) vel_track_err_max_time = innov_time[vel_track_err_max_arg] vel_track_err_max = np.amax(ekf2_innovations['output_tracking_error[1]']) pos_track_err_max_arg = np.argmax(ekf2_innovations['output_tracking_error[2]']) pos_track_err_max_time = innov_time[pos_track_err_max_arg] pos_track_err_max = np.amax(ekf2_innovations['output_tracking_error[2]']) plt.subplot(3, 1, 1) plt.plot(1e-6 * ekf2_innovations['timestamp'], 1e3 * ekf2_innovations['output_tracking_error[0]'], 'b') plt.title('Output Observer Tracking Error Magnitudes') plt.ylabel('angles (mrad)') plt.grid() plt.text(ang_track_err_max_time, 1e3 * ang_track_err_max, 'max=' + str(round(1e3 * ang_track_err_max, 2)), fontsize=12, horizontalalignment='left', verticalalignment='top') plt.subplot(3, 1, 2) plt.plot(1e-6 * ekf2_innovations['timestamp'], ekf2_innovations['output_tracking_error[1]'], 'b') plt.ylabel('velocity (m/s)') plt.grid() plt.text(vel_track_err_max_time, vel_track_err_max, 'max=' + str(round(vel_track_err_max, 2)), fontsize=12, horizontalalignment='left', verticalalignment='top') plt.subplot(3, 1, 3) plt.plot(1e-6 * ekf2_innovations['timestamp'], ekf2_innovations['output_tracking_error[2]'], 'b') plt.ylabel('position (m)') plt.xlabel('time (sec)') 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(20, figsize=(20, 13)) plt.subplot(2, 1, 1) plt.plot(1e-6 * estimator_status['timestamp'], estimator_status['states[22]'], 'b') plt.title('Wind Velocity Estimates') plt.ylabel('North (m/s)') plt.xlabel('time (sec)') plt.grid() plt.subplot(2, 1, 2) plt.plot(1e-6 * estimator_status['timestamp'], estimator_status['states[23]'], 'b') plt.ylabel('East (m/s)') plt.xlabel('time (sec)') plt.grid() pp.savefig() plt.close(20) # close the pdf file pp.close() # don't display to screen # plt.show() # clase all figures plt.close("all") # Do some automated analysis of the status data # find a late/early index range from 5 sec after in_air_transtion_time to 5 sec before on-ground transition time for mag and optical flow checks to avoid false positives # this can be used to prevent false positives for sensors adversely affected by close proximity to the ground late_start_index = np.amin(np.where(status_time > (in_air_transition_time + 5.0))) early_end_index = np.amax(np.where(status_time < (on_ground_transition_time - 5.0))) num_valid_values_trimmed = (early_end_index - late_start_index + 1) # normal index range is defined by the flight duration start_index = np.amin(np.where(status_time > in_air_transition_time)) end_index = np.amax(np.where(status_time < on_ground_transition_time)) num_valid_values = (end_index - start_index + 1) # also find the start and finish indexes for the innovation data innov_late_start_index = np.amin(np.where(innov_time > (in_air_transition_time + 5.0))) innov_early_end_index = np.amax(np.where(innov_time < (on_ground_transition_time - 5.0))) innov_num_valid_values_trimmed = (innov_early_end_index - innov_late_start_index + 1) innov_start_index = np.amin(np.where(innov_time > in_air_transition_time)) innov_end_index = np.amax(np.where(innov_time < on_ground_transition_time)) innov_num_valid_values = (innov_end_index - innov_start_index + 1) # define dictionary of test results and descriptions test_results = { 'master_status': ['Pass', 'Master check status which can be either Pass Warning or Fail. 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_sensor_status': ['Pass', 'Magnetometer 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'], 'yaw_sensor_status': ['Pass', 'Yaw sensor check summary. This sensor data can be sourced from the magnetometer or an external vision system. 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'], 'vel_sensor_status': ['Pass', 'Velocity 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'], 'pos_sensor_status': ['Pass', 'Position 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'], 'hgt_sensor_status': ['Pass', 'Height sensor check summary. This sensor data can be sourced from either Baro or GPS or range finder or external vision system. 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 anomalies were detected and no further investigation is required'], 'hagl_sensor_status': ['Pass', 'Height above ground sensor check summary. This sensor data is normally sourced from a rangefinder sensor. 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'], 'tas_sensor_status': ['Pass', 'Airspeed 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'], 'imu_sensor_status': ['Pass', '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'), 'The percentage of in-flight consolidated magnetic field sensor innovation consistency test values > 0.5.'], 'magx_fail_percentage': [float('NaN'), 'The percentage of in-flight recorded failure events for the X-axis magnetic field sensor innovation consistency test.'], 'magy_fail_percentage': [float('NaN'), 'The percentage of in-flight recorded failure events for the Y-axis magnetic field sensor innovation consistency test.'], 'magz_fail_percentage': [float('NaN'), 'The percentage of in-flight recorded failure events for the Z-axis magnetic field sensor innovation consistency test.'], 'yaw_fail_percentage': [float('NaN'), 'The percentage of in-flight recorded failure events for the yaw sensor innovation consistency test.'], 'mag_test_max': [float('NaN'), 'The maximum in-flight value of the magnetic field sensor innovation consistency test ratio.'], 'mag_test_mean': [float('NaN'), 'The mean in-flight value of the magnetic field sensor innovation consistency test ratio.'], 'vel_percentage_red': [float('NaN'), 'The percentage of in-flight velocity sensor consolidated innovation consistency test values > 1.0.'], 'vel_percentage_amber': [float('NaN'), 'The percentage of in-flight velocity sensor consolidated innovation consistency test values > 0.5.'], 'vel_fail_percentage': [float('NaN'), 'The percentage of in-flight recorded failure events for the velocity sensor consolidated innovation consistency test.'], 'vel_test_max': [float('NaN'), 'The maximum in-flight value of the velocity sensor consolidated innovation consistency test ratio.'], 'vel_test_mean': [float('NaN'), 'The mean in-flight value of the velocity sensor consolidated innovation consistency test ratio.'], 'pos_percentage_red': [float('NaN'), 'The percentage of in-flight position sensor consolidated innovation consistency test values > 1.0.'], 'pos_percentage_amber': [float('NaN'), 'The percentage of in-flight position sensor consolidated innovation consistency test values > 0.5.'], 'pos_fail_percentage': [float('NaN'), 'The percentage of in-flight recorded failure events for the velocity sensor consolidated innovation consistency test.'], 'pos_test_max': [float('NaN'), 'The maximum in-flight value of the position sensor consolidated innovation consistency test ratio.'], 'pos_test_mean': [float('NaN'), 'The mean in-flight value of the position sensor consolidated innovation consistency test ratio.'], 'hgt_percentage_red': [float('NaN'), 'The percentage of in-flight height sensor innovation consistency test values > 1.0.'], 'hgt_percentage_amber': [float('NaN'), 'The percentage of in-flight height sensor innovation consistency test values > 0.5.'], 'hgt_fail_percentage': [float('NaN'), 'The percentage of in-flight recorded failure events for the height sensor innovation consistency test.'], 'hgt_test_max': [float('NaN'), 'The maximum in-flight value of the height sensor innovation consistency test ratio.'], 'hgt_test_mean': [float('NaN'), 'The mean in-flight value of the height sensor innovation consistency test ratio.'], 'tas_percentage_red': [float('NaN'), 'The percentage of in-flight airspeed sensor innovation consistency test values > 1.0.'], 'tas_percentage_amber': [float('NaN'), 'The percentage of in-flight airspeed sensor innovation consistency test values > 0.5.'], 'tas_fail_percentage': [float('NaN'), 'The percentage of in-flight recorded failure events for the airspeed sensor innovation consistency test.'], 'tas_test_max': [float('NaN'), 'The maximum in-flight value of the airspeed sensor innovation consistency test ratio.'], 'tas_test_mean': [float('NaN'), 'The mean in-flight value of the airspeed sensor innovation consistency test ratio.'], 'hagl_percentage_red': [float('NaN'), 'The percentage of in-flight height above ground sensor innovation consistency test values > 1.0.'], 'hagl_percentage_amber': [float('NaN'), 'The percentage of in-flight height above ground sensor innovation consistency test values > 0.5.'], 'hagl_fail_percentage': [float('NaN'), 'The percentage of in-flight recorded failure events for the height above ground sensor innovation consistency test.'], 'hagl_test_max': [float('NaN'), 'The maximum in-flight value of the height above ground sensor innovation consistency test ratio.'], 'hagl_test_mean': [float('NaN'), 'The mean in-flight value of the height above ground sensor innovation consistency test ratio.'], '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.'], 'filter_faults_max': [float('NaN'), 'Largest recorded value of the filter internal fault bitmask. Should always be zero.'], 'imu_coning_peak': [float('NaN'), 'Peak in-flight value of the IMU delta angle coning vibration metric (rad)'], 'imu_coning_mean': [float('NaN'), 'Mean in-flight value of the IMU delta angle coning vibration metric (rad)'], 'imu_hfdang_peak': [float('NaN'), 'Peak in-flight value of the IMU delta angle high frequency vibration metric (rad)'], '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_median': [float('NaN'), 'Median 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_pos_err_median': [float('NaN'), 'Median in-flight value of the output observer position error (m)'], 'imu_dang_bias_median': [float('NaN'), 'Median in-flight value of the delta angle bias vector length (rad)'], 'imu_dvel_bias_median': [float('NaN'), 'Median in-flight value of the delta velocity bias vector length (m/s)'], '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.'], 'on_ground_transition_time': [round(on_ground_transition_time, 1), '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.'], } # generate test metadata # reduction of innovation message data 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 + 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 + 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 + 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 + 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 + 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 + 1]) # Magnetometer Sensor Checks if (np.amax(yaw_aligned) > 0.5): 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 + 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 + 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 + 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 + 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 + 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] > 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 + 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 + 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] > 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 + 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 + 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] > 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 + 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 + 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] > 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 + 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 + 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] > 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 + 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 + 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 + 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 + 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( 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']) # TODO - process the following bitmask's when they have been properly documented in the uORB topic # estimator_status['health_flags'] # estimator_status['timeout_flags'] # calculate a master status - Fail, Warning, Pass # check test results against levels to provide a master status # check for warnings if (test_results.get('mag_percentage_amber')[0] > check_levels.get('mag_amber_warn_pct')): test_results['master_status'][0] = 'Warning' test_results['mag_sensor_status'][0] = 'Warning' if (test_results.get('vel_percentage_amber')[0] > check_levels.get('vel_amber_warn_pct')): test_results['master_status'][0] = 'Warning' test_results['vel_sensor_status'][0] = 'Warning' if (test_results.get('pos_percentage_amber')[0] > check_levels.get('pos_amber_warn_pct')): test_results['master_status'][0] = 'Warning' test_results['pos_sensor_status'][0] = 'Warning' if (test_results.get('hgt_percentage_amber')[0] > check_levels.get('hgt_amber_warn_pct')): test_results['master_status'][0] = 'Warning' test_results['hgt_sensor_status'][0] = 'Warning' if (test_results.get('hagl_percentage_amber')[0] > check_levels.get('hagl_amber_warn_pct')): test_results['master_status'][0] = 'Warning' test_results['hagl_sensor_status'][0] = 'Warning' 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 (test_results.get('imu_hfdang_mean')[0] > check_levels.get('imu_hfdang_mean_warn')) or (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 - Vibration' if ((test_results.get('imu_dang_bias_median')[0] > check_levels.get('imu_dang_bias_median_warn')) or (test_results.get('imu_dvel_bias_median')[0] > check_levels.get('imu_dvel_bias_median_warn'))): test_results['master_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 (test_results.get('magy_fail_percentage')[0] > check_levels.get('mag_fail_pct')) or (test_results.get('magz_fail_percentage')[0] > check_levels.get('mag_fail_pct')) or (test_results.get('mag_percentage_amber')[0] > check_levels.get('mag_amber_fail_pct'))): test_results['master_status'][0] = 'Fail' test_results['mag_sensor_status'][0] = 'Fail' if (test_results.get('yaw_fail_percentage')[0] > check_levels.get('yaw_fail_pct')): test_results['master_status'][0] = 'Fail' test_results['yaw_sensor_status'][0] = 'Fail' if ((test_results.get('vel_fail_percentage')[0] > check_levels.get('vel_fail_pct')) or (test_results.get('vel_percentage_amber')[0] > check_levels.get('vel_amber_fail_pct'))): test_results['master_status'][0] = 'Fail' test_results['vel_sensor_status'][0] = 'Fail' if ((test_results.get('pos_fail_percentage')[0] > check_levels.get('pos_fail_pct')) or (test_results.get('pos_percentage_amber')[0] > check_levels.get('pos_amber_fail_pct'))): test_results['master_status'][0] = 'Fail' test_results['pos_sensor_status'][0] = 'Fail' if ((test_results.get('hgt_fail_percentage')[0] > check_levels.get('hgt_fail_pct')) or (test_results.get('hgt_percentage_amber')[0] > check_levels.get('hgt_amber_fail_pct'))): test_results['master_status'][0] = 'Fail' test_results['hgt_sensor_status'][0] = 'Fail' if ((test_results.get('tas_fail_percentage')[0] > check_levels.get('tas_fail_pct')) or (test_results.get('tas_percentage_amber')[0] > check_levels.get('tas_amber_fail_pct'))): test_results['master_status'][0] = 'Fail' test_results['tas_sensor_status'][0] = 'Fail' if ((test_results.get('hagl_fail_percentage')[0] > check_levels.get('hagl_fail_pct')) or (test_results.get('hagl_percentage_amber')[0] > check_levels.get('hagl_amber_fail_pct'))): test_results['master_status'][0] = 'Fail' test_results['hagl_sensor_status'][0] = 'Fail' if ((test_results.get('ofx_fail_percentage')[0] > check_levels.get('flow_fail_pct')) or (test_results.get('ofy_fail_percentage')[0] > check_levels.get('flow_fail_pct'))): test_results['master_status'][0] = 'Fail' test_results['flow_sensor_status'][0] = 'Fail' if (test_results.get('filter_faults_max')[0] > 0): test_results['master_status'][0] = 'Fail' test_results['filter_fault_status'][0] = 'Fail' return test_results