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Sensors 2012, 12(7), 9566-9585; doi:10.3390/s120709566
Article

Adaptive UAV Attitude Estimation Employing Unscented Kalman Filter, FOAM and Low-Cost MEMS Sensors

1
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2,*  and 2
Received: 21 May 2012 / Revised: 2 July 2012 / Accepted: 9 July 2012 / Published: 13 July 2012
(This article belongs to the Special Issue Transducer Systems)
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Abstract

Navigation employing low cost MicroElectroMechanical Systems (MEMS) sensors in Unmanned Aerial Vehicles (UAVs) is an uprising challenge. One important part of this navigation is the right estimation of the attitude angles. Most of the existent algorithms handle the sensor readings in a fixed way, leading to large errors in different mission stages like take-off aerobatic maneuvers. This paper presents an adaptive method to estimate these angles using off-the-shelf components. This paper introduces an Attitude Heading Reference System (AHRS) based on the Unscented Kalman Filter (UKF) using the Fast Optimal Attitude Matrix (FOAM) algorithm as the observation model. The performance of the method is assessed through simulations. Moreover, field experiments are presented using a real fixed-wing UAV. The proposed low cost solution, implemented in a microcontroller, shows a satisfactory real time performance.
Keywords: UAV navigation; attitude estimation; unscented Kalman filter; attitude heading reference system; fast optimal attitude matrix UAV navigation; attitude estimation; unscented Kalman filter; attitude heading reference system; fast optimal attitude matrix
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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de Marina, H.G.; Espinosa, F.; Santos, C. Adaptive UAV Attitude Estimation Employing Unscented Kalman Filter, FOAM and Low-Cost MEMS Sensors. Sensors 2012, 12, 9566-9585.

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