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.
Highlights
Due to their advantages of high-maneuverability and long flight range, high-speed unmanned aerial vehicles (UAVs) are widely used in scout, tracking, danger monitoring, etc. [1]
The filtering error obtained by Sage–Husa adaptive Kalman filter (SHAKF) increases rapidly during 250 s~300 s, but the filtering results converge after this period; the anti-interference performance of H∞ filter and strong tracking filter (STF) is better, as there is no apparent fluctuation in error during 250 s~300 s
The error obtained by H∞ filter is larger, especially the error of position after 300 s, where a severe fluctuation appears; by contrast, strong adaptive Kalman filter (SAKF) performs in a stable state during the whole simulation period, and its accuracy is higher than STF
Summary
Due to their advantages of high-maneuverability and long flight range, high-speed unmanned aerial vehicles (UAVs) are widely used in scout, tracking, danger monitoring, etc. [1]. 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. 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. 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∞ 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.
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