# An Integrated Adaptive Kalman Filter for High-Speed UAVs
## 3 Verification and Analysis
#### 3.1.3 Results and Analysis of Simulation  
According to the simulation, the diagrams of errors (position, velocity and attitude) obtained by four types of filtering algorithms in scene 1 are given as Figure 3; Figure 4 shows the errors in scene 2. According to the results shown in the tables and diagrams above, in scene 1, no filtering divergence appears, but the attitude error increases in each case. The filtering error obtained by SHAKF increases rapidly during 250 s-300 s, but the filtering results converge after this period; the anti-interference performance of H\({}_{\infty}\) filter and STF is better, as there is no apparent fluctuation in error during 250 s-300 s. However, the error obtained by H\({}_{\infty}\) filter is larger, especially the error of position after 300 s, where a severe fluctuation appears; by contrast, SAKF performs in a stable state during the whole simulation period, and its accuracy is higher than STF. For attitude, we noticed that the error of yaw is always in a relatively high level. There are two reasons to explain such situation. On one hand, the system did not use the information provided by GNSS to fix the yaw angle. On the other hand, because of the filtering constraint, the error of attitude was always at a controllable range without divergence. During 250 s-300 s the anomaly of inertial sensors occurs, which influences the accuracy of calculated yaw. As H\({}_{\infty}\) is relatively insensitive to abrupt change, it seems that the error of yaw is lower than STF and SAKF. In scene 2, we lowered the accuracy of GNSS during 100-150 s and set a hopping point during the period of 230-232 s. Based on barometer and inertial sensors, we used the complementary filter to calculate the vertical position and velocity, so they were hardly influenced by anomalies of GNSS during the period of 100-150 s; At the same time, the horizontal estimation accuracy of STF and SHAKF deteriorate rapidly into the H\({}_{\infty}\) filter level; SAKF has the best performance in this period. During 230 s-232 s, SHAKF and STF are sensitive to the hopping point. STF is more easily influenced by hopping point than SHAKF but it also converges faster after the anomaly. On the other hand, the H\({}_{\infty}\) filter is insensitive to this situation. Since SAKF shares similar robustness with H\({}_{\infty}\) filter, it is hardly influenced by the hopping point. In summary, the SHAKF has the best accuracy without interference, but its accuracy is rapidly reduced when interference is introduced, even filter divergence may occur. The STF relies more on the measurement information, which is insensitive to the inertial anomaly. However it is easily influenced by the anomaly of GNSS. H\({}_{\infty}\) filter guarantees the stability in interference and shows high robustness when facing an anomaly, whereas its performance is limited by low-accuracy. The proposed algorithm, SAKF, which combines the advantages of three filtering methods, well fits for the high-dynamic flight of the UAV. Figure 3: The diagrams of errors obtained by four types of filtering algorithms in scene 1 designed in 3.1.2. (**a**) Sage–Husa adaptive Kalman filter (SHAKF); (**b**) strong tracking filter (STF); (**c**) H\({}_{\infty}\) filter; (**d**) strong adaptive Kalman filter (SAKF). To make a more accurate comparison, the RMS of four algorithms in two scenes is listed in Tables 2 and 3.