An Integrated Adaptive Kalman Filter for High-Speed UAVs
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Target Tracking and Data Fusion in Sensor Networks · Inertial Sensor and Navigation · Robotics and Sensor-Based Localization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310007, China
Funding
China Aerospace Science and Technology Foundation
References (20)
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