# An Integrated Adaptive Kalman Filter for High-Speed UAVs
This will cause the UAV to be guided with a wrong route or even to crash [5]. If the value of Q-matrix or R-matrix is only adjusted blindly, the weight of real-time measurement and one-step prediction may be more appropriate to some extent, but it is impossible to achieve the optimal estimation. To solve the aforementioned problems of KF, changing the intermediate steps of KF is an effective way to improve the robustness and self-adaptive ability of the filtering algorithm. Aiming at adaptive estimation of Q-matrix and R-matrix, the Sage-Husa adaptive Kalman filter (SHAKF) was proposed [9; 10; 11]. Theoretically, this algorithm adaptively estimates the noise characteristic of system and measurement, and solves the uncertainty of KF for noise estimation. However, the noise covariance matrix is prone to losing the positive semi-definiteness and causes filtering divergence. The strong tracking filter (STF), trying to refine the priori estimate error covariance matrix (P-matrix), imports fading factor to reduce the influence of priori and historical information to current state estimation [12]. This method is good at tracking the abrupt changes. In reference [13], researchers used STF for sensor fault diagnosis in a UAV control system, and the simulation results showed good performance in real-time and accuracy. The reference [14; 15] proposed an adaptive fuzzy strong tracking extended Kalman filter structure which combines STF and fuzzy logic model. This method was respectively used in pure Global Position System (GPS) navigation and Inertial Navigation System (INS)/GPS integrated navigation, which could improve the self-adaptive ability of system. But the measuring problem of a real vehicle in high-dynamic environment is not discussed. In addition, robust filtering methods such as H\({}_{\infty}\) filter were also used in practical system [16; 17; 18]. The H\({}_{\infty}\) filter could minimize the influence of noise and initial state uncertainty, although Q-matrix, R-matrix and P0-matrix are unknown [19]. Its robustness can guarantee its availability, even in the worst case. The algorithm is suitable for all possible external interference, though it sacrifices average accuracy to maintain its stability [20]. In order to take the anti-interference ability and the accuracy of the filtering algorithm into account, this paper proposed an integrated filtering algorithm--strong adaptive Kalman filter (SAKF), based on SHAKF, STF and H\({}_{\infty}\) filter. The algorithm guarantees the stability and accuracy in high-dynamic flight of UAV, when the measurement noise is uncertain and the system's interference is unavoidable because of the body vibration. SAKF adopts the method of SHAKF to realize adaptive estimation of noise covariance matrices. The R-matrix is considered as the criterion as to whether the filtering process is abnormal or not. Under normal condition, the STF is selected to provide high filtering accuracy. In abnormal situation, H\({}_{\infty}\) filter is used to guarantee the stability of filter. The key innovation of this filtering algorithm is integration. In the process of our research we found that single filtering algorithm can hardly achieve the best performance in every aspect. So integrating the isolating algorithms is a feasible solution. Comparing with SHAKF, SAKF has a better tracking ability, which means it can closely track the abrupt change in high-dynamic flight. So SHAKF is better at adaptivity than SHAKF. Compared with STF, SAKF is more stable, which means such a method is not easy to diverge and can withstand unexpected interference; Compared with H\({}_{\infty}\) filter, SAKF has a higher accuracy, so this method can provide a better navigation. All of these advantages make the integrated filtering algorithms have good performance for high-dynamic navigation. To verify the superiority of SAKF, simulations and practical experiments were designed to compare the performance of different filter algorithms.