Online from: 1929
Subject Area: Mechanical & Materials Engineering
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|Title:||Attitude and heading reference system with acceleration compensation|
|Author(s):||Elder M. Hemerly, (Systems and Control Department, Electronics Division, Technological Institute of Aeronautics, São José dos Campos, Brazil), Benedito C.O. Maciel, (FT-Flight Technologies, São dos Campos, Brazil), Anderson de P. Milhan, (Navcon-Navigation and Control, São José dos Campos, Brazil), Valter R. Schad, (Navcon-Navigation and Control, São José dos Campos, Brazil)|
|Citation:||Elder M. Hemerly, Benedito C.O. Maciel, Anderson de P. Milhan, Valter R. Schad, (2012) "Attitude and heading reference system with acceleration compensation", Aircraft Engineering and Aerospace Technology, Vol. 84 Iss: 2, pp.87 - 93|
|Keywords:||acceleration and rate gyros sensors, Aerospace technology, Attitude and heading reference system, Extended Kalman filter, Magnetic, MARG sensors, Mathematics, Navigation, Programming and algorithm theory, Quaternion, Sensors|
|Article type:||Research paper|
|DOI:||10.1108/00022661211207893 (Permanent URL)|
|Publisher:||Emerald Group Publishing Limited|
|Acknowledgements:||The authors would like to thank CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) and Finep (Financiadora de Estudos e Projetos) for the financial support.|
Purpose – The purpose of this paper is to employ an extended Kalman filter for implementing an AHRS (attitude and heading reference system) with acceleration compensation, thereby improving the reliability of such systems, since this removes the usual restrictive assumption that the vehicle is undergoing a non-accelerated maneuver.
Design/methodology/approach – MARG (magnetic, acceleration and rate gyros) sensors constitute the basic hardware, which are integrated by the Kalman filter. The error dynamics for attitude and gyro biases is obtained in the navigation frame, providing a much simpler approach than usually taken in the literature, since it relies on direct quaternion differentiation. The state vector associated to the error dynamics possesses six components: three are associated to the quaternion error and three concern gyro bias estimates.
Findings – The AHRS is implemented in an ARM (Advanced RISC Machine) processor and tested with experimental data. The accelerated case is treated by two complementary approaches: by changing the noise variance in the Kalman filter, and by obtaining an acceleration information from GPS (global positioning system) velocity measurements. Experimental results are presented and the performance is compared with commercial ARHS systems.
Practical implications – The proposed AHRS can be implemented with low cost MARG sensors, and GPS aiding, with use for instance in UAV (unmanned aerial vehicle) and small aircrafts' attitude estimation, for navigation and control applications.
Originality/value – Usually the AHRS designs employ as states total gyro bias and Euler angles, or quaternion, and do not consider the accelerated case. Here the state is comprised by gyro bias and quaternion error variables, which attenuates the effect of nonlinearities, and two complementary procedures tackle the accelerated case: acceleration correction by using a GPS derived acceleration signal and change in the output noise covariance used by the Kalman filter.
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