NONLINEAR ATTITUDE AND GYROSCOPE'S BIAS ESTIMATION FOR A VTOL UAV

2005 ◽  
Vol 38 (1) ◽  
pp. 283-288 ◽  
Author(s):  
Jean Michel Pflimlin ◽  
Tarek Hamel ◽  
Philippe Souères ◽  
Najib Metni
Keyword(s):  
Bias Estimation ◽  
Vtol Uav

Attitude and gyro bias estimation for a VTOL UAV

2006 ◽  
Vol 14 (12) ◽  
pp. 1511-1520 ◽  
Author(s):  
Najib Metni ◽  
Jean-Michel Pflimlin ◽  
Tarek Hamel ◽  
Philippe Souères
Keyword(s):  
Bias Estimation ◽  
Vtol Uav ◽  
Gyro Bias

scholarly journals Nonlinear attitude and gyroscope's bias estimation for a VTOL UAV

2007 ◽  
Vol 38 (3) ◽  
pp. 197-210 ◽  
Author(s):  
J. M. Pflimlin ◽  
T. Hamel ◽  
P. Souères
Keyword(s):  
Bias Estimation ◽  
Vtol Uav

Sensor Fusion for Attitude and Bias Estimation for a VTOL UAV

2010 ◽  
Author(s):  
Najib A. Metni
Keyword(s):  
Inertial Measurement Unit ◽  
Low Cost ◽  
Measurement Unit ◽  
Bias Estimation ◽  
Inertial Measurement ◽  
Complementary Filter ◽  
Vtol Uav ◽  
Orientation Matrix ◽  
Local Parametrization ◽  
Aerial Vehicle

In this paper, a nonlinear complementary filter is presented to estimate the attitude of a Vertical Take Off and Landing Unmanned Aerial Vehicle (VTOL UAV). The measurements are taken from a low-cost IMU (Inertial Measurement Unit) which consists of 3-axis accelerometers, 3-axis gyroscopes and 3-axis magnetometers. From the proposed estimators, the full orientation matrix R will be retrieved. The proposed observers will estimate the instantaneous quaternions as well as the gyroscope bias. This representation of orientation by the rotation matrix and quaternions allows overcoming the problem of singularities that appear in local parametrization such as Euler angles. Therefore, both estimators may be used to describe any kind of 3-D motion. Convergence of the two observers is theoretically proved and simulations are conducted taking data from a real platform in hovering flight conditions.

Separate-bias estimation with reduced-order Kalman filters

1998 ◽  
Vol 43 (7) ◽  
pp. 983-987 ◽  
Author(s):  
D. Haessig ◽  
B. Friedland
Keyword(s):  
Kalman Filters ◽  
Bias Estimation ◽  
Reduced Order
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