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An Integrated Adaptive Kalman Filter for High-Speed UAVs

College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310007, China
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Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Appl. Sci. 2019, 9(9), 1916; https://doi.org/10.3390/app9091916
Received: 28 March 2019 / Revised: 30 April 2019 / Accepted: 2 May 2019 / Published: 9 May 2019
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAVs))
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Abstract

In order to solve the problems of filtering divergence and low accuracy in Kalman filter (KF) applications in a high-speed unmanned aerial vehicle (UAV), this paper proposed a new method of integrated robust adaptive Kalman filter: strong adaptive Kalman filter (SAKF). The simulation of two high-dynamic conditions and a practical experiment were designed to verify the new multi-sensor data fusion algorithm. Then the performance of the Sage–Husa adaptive Kalman filter (SHAKF), strong tracking filter (STF), H filter and SAKF were compared. The results of the simulation and practical experiments show that the SAKF can automatically select its filtering process under different conditions, according to an anomaly criterion. SAKF combines the advantages of SHAKF, H filter and STF, and has the characteristics of high accuracy, robustness and good tracking skill. The research has proved that SAKF is more appropriate in high-speed UAV navigation than single filter algorithms. View Full-Text
Keywords: high-speed UAV; high-dynamic condition; integrated navigation system; adaptive Kalman filter; H filter; strong tracking filter high-speed UAV; high-dynamic condition; integrated navigation system; adaptive Kalman filter; H filter; strong tracking filter
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Huang, T.; Jiang, H.; Zou, Z.; Ye, L.; Song, K. An Integrated Adaptive Kalman Filter for High-Speed UAVs. Appl. Sci. 2019, 9, 1916.

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