# An Integrated Adaptive Kalman Filter for High-Speed UAVs
## 4 Conclusions  
In this paper, an integrated adaptive filtering algorithm named SAKF is proposed, and the effectiveness of the algorithm is verified by simulation and a practical experiment, respectively. The results of the simulation and experiment showed that the SAKF has a better performance than other three algorithms in high-speed UAV integrated navigation system. It has the superiority of adapting to different disturbances during high-dynamic flight, and estimating measurement noise and system noise in real time. Meanwhile, it has the advantages of high accuracy, tracking ability and robustness. The proposed algorithm provides a new solution for a high-speed UAV navigation system. At present, most of the data-fusion algorithms for an integrated navigation system in high-dynamic environment are still immature. Their verification is mostly based on pure simulation. We designed a real hardware testing platform, and used the real navigation data to verify its performance. We have proven the value of the proposed algorithm in the practical application in a high-speed UAV. In future, we will focus more on the real time and online navigation for UAV in high-dynamic conditions using SAKF. The further research of this new adaptive Kalman filtering algorithm will promote the development of high-speed UAVs and other fields of navigation. Conceptualization, T.H. and H.J. ; Methodology, T.H. and H.J. ; Software, H.J. ; Validation, H.J. and Z.Z. ; Formal Analysis, Z.Z. ; Writing--Original Draft Preparation, H.J. and Z.Z. ; Writing--Review and Editing, T.H. and Z.Z. ; Project Administration, T.H. and L.Y. ; Supervision, K.S. This research was funded by China Aerospace Science and Technology Foundation, grant number 2018-HT-ZD-06. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} KF & Kalman filter \\ UAV & Unmanned Aerial Vehicle \\ SAKF & Strong Adaptive Kalman filter \\ SHAKF & Sage-Husa Adaptive Kalman filter \\ STF & Strong Tracking filter \\ SINS & Strap-down Inertial Navigation System \\ GNSS & Global Navigation Satellite System \\ GPS & Global Position System \\ INS & Inertial Navigation System \\ ENU & East-North-Up \\ DSP & Digital Signal Processor \\ FPGA & Field-Programmable Gate Array \\ EMIF & External Memory Interface \\ \end{tabular}   
* _[PERSON] et al. (2012)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] SINS/GPS/CNS information fusion system based on improved Huber filter with classified adaptive factors for high-speed UAVs. In Proceedings of the Position Location & Navigation Symposium, Myrtle Beach, SC, USA, 23-26 April 2012; pp. 441-446. * [PERSON] and [PERSON] (2009) [PERSON] [PERSON]; [PERSON] On precise positioning method for highly dynamic GPS/SINS integrated navigation. _Inf. Control_**2009**, _38_, 303-304. * _[PERSON] et al. (2012)_ [PERSON]; [PERSON]; [PERSON] Particle filter-based AUV integrated navigation methods. _Robot_**2012**, _34_, 80-85. [CrossRef]
* _[PERSON] et al. (2017)_ [PERSON]; [PERSON]; [PERSON] _Research on Error Analysis and Optimization Methods for Strapdown Inertial Navigation Algorithm under Highly Dynamic Environment_; National Defense Industry Press: Beijing, China, 2017. * _[PERSON] and [PERSON] (1969)_ [PERSON]; [PERSON] Algorithms for sequential adaptive estimation of prior statistics. In Proceedings of the Adaptive Processes, University Park, PA, USA, 17-19 November 1969. * _[PERSON] (2012)_ [PERSON] An improved real-time adaptive Kalman filter for low-cost integrated GPS/INS navigation. In Proceedings of the International Conference on Measurement, Harbin, China, 18-20 May 2012. * _[PERSON] and [PERSON] (2013)_ [PERSON]; [PERSON] Robust adaptive kalman filter for estimation of UAV dynamics in the presence of sensor/actuator faults. _Dialogues Cardiovasc. Med. DCM_**2013**, _28_, 376-383.