Rs to the maximum attitude error. two The bias estimation error refers towards the largest with the 3 gyros or accelerometers.Table 2. The relative error, primarily based around the non-covariance transformation in six experiments. Experiment Number 1 2 three four 5 6 average Attitude Error/ 3.34 2.89 five.56 4.45 two.56 5.56 four.06 Position Error/m 2.four two.09 1.26 two.47 1.76 0.89 1.811667 Accelerometer Bias Estimation Error/ 59.3 62.0 20.0 62.five 61.7 22.8 48.1 Gyro Bias Estimation Error/( /h) 0.0091 0.0094 0.0215 0.0069 0.0038 0.0019 0.To sum up, when the navigation frame alterations directly, the integrated navigation 7-Hydroxymethotrexate Data Sheet outcomes show severe fluctuation, taking a lot more than an hour to attain stability again. The lower the observability from the error state, the larger the error amplitude. The integrated navigation benefits, primarily based on the covariance transformation approach, do not fluctuate during the modify on the navigation frame, that is consistent together with the reference benefits. Experimental benefits confirm the effectiveness of the proposed algorithm. four.two. Semi-Physical Simulation Experiment Pure mathematical simulation is difficult to use to accurately simulate an actual scenario. As a result, a virtual polar-region process is used to convert the measured aviation information to 80 latitude, to obtain semi-physical simulation information [20]. In this way, the reliability of your algorithm at high latitudes could be verified. In this simulation, the navigation outcome based on the G-frame is made use of as a reference, which will stay away from the decrease of algorithm accuracy caused by the rise in latitude. The simulation benefits, primarily based on the covariance transformation and non-covariance transformation, are shown in Figure 4. As is often seen in Figure 4a, amongst the attitude errors, the relative yaw error will be the biggest. The relative yaw error reaches 5 `without covariance transformation. The integrated navigation result with covariance transformation has a less relative yaw error of 0.2′. As shown in Figure 4b, the relative position error is 12 m, without the need of covariance transformation. The integrated navigation result with covariance transformation shows much better stability plus a smaller relative position error of eight m. As shown in Figure 4c,d, the maximum bias error on the gyroscope with and with no covariance transformation reached 0.001 /h and 0.02 /h, respectively. The maximum bias error with the accelerometer, with and without covariance transformation, reached 0.1 and 25 , respectively.Appl. Sci. 2021, 11,situation. Hence, a virtual polar-region strategy is applied to convert the measured aviation data to 80latitude, to receive semi-physical simulation data [20]. Within this way, the reliability from the algorithm at high latitudes is often verified. Within this simulation, the navigation outcome based on the G-frame is utilized as a reference, which will stay away from the decrease of algorithm ten of 11 accuracy caused by the rise in latitude. The simulation final results, based around the covariance transformation and non-covariance transformation, are shown in Figure 4.Appl. Sci. 2021, 11,11 of(a)(b)(c)(d)Figure 4. The simulation outcomes, based around the covariance transformation and non-covariance transformation. (a) Figure 4. The simulation benefits, primarily based on the covariance transformation and non-covariance transformation. (a) The The relative error of attitude; (b) the relative error of position; (c) the relative error of gyro bias estimation; (d) the relative error relative error of attitude; (b) the relative error of position; (c) the relative error of gyro bias e.
