Model-Aided Synthetic Airspeed Estimation of UAVs for Analytical Redundancy

This letter proposes a novel method for model-aided synthetic airspeed estimation of UAVs. The major contribution of the proposed algorithm is that the synthetic airspeed measurement is newly formulated for analytical redundancy. This filter only requires inertial measurement unit (IMU), airflow ang...

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Bibliographic Details
Published in:IEEE robotics and automation letters 2021-07, Vol.6 (3), p.5841-5848
Main Authors: Youn, Wonkeun, Ryu, Hanseok, Jang, Dongjin, Lee, Changho, Park, Youngmin, Lee, Dongjin, Rhudy, Matthew B.
Format: Article
Language:English
Subjects:
Aerodynamic coefficients
Aerodynamics
Air flow
Aircraft
Aircraft models
Airspeed
Airspeed estimation
Algorithms
Analytical models
analytical redundancy
Atmospheric modeling
complementary filter
Dynamic models
Estimation
Global Positioning System
Inertial platforms
Kalman filters
Redundancy
Unmanned aerial vehicles
unmanned aerial vehicles (UAVs)
unscented Kalman filter
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Summary:This letter proposes a novel method for model-aided synthetic airspeed estimation of UAVs. The major contribution of the proposed algorithm is that the synthetic airspeed measurement is newly formulated for analytical redundancy. This filter only requires inertial measurement unit (IMU), airflow angles, and elevator control input along with a simple aircraft model containing only three lift coefficient parameters; no GPS or complex aircraft dynamic model are required. Particularly, two novel filters (unscented Kalman filter and complementary filter) are proposed and evaluated without direct airspeed and GPS measurements. Flight test results of a UAV demonstrated that the proposed algorithm yields accurate estimated airspeed, demonstrating its effectiveness for analytical redundancy.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2021.3086428