«Cubature & gauss-hermite» based kalman filters applied to unmanned aerial vehicle attitude estimation problem

Аuthors

Benzerrouk H. -.^1*, Nebylov A. V.^1**, Salhi H. -.^2***, Closas P. -.^3****

1. Saint Petersburg State University of Aerospace Instrumentation, 67, Bolshaya Morskaya str., Saint Petersburg, 190000, Russia
2. SET Laboratory ( Systemes Electriques et Telecommande) of Electronic Department of Saad Dahlab University of Blida, BP 270, Blida, Soumaa, 9000, Algeria
3. Centre Tecnologic de Telecomunicacions de Catalunya (CTTC), 7, av. Carl Friedrich Gauss, Castelldefels, 08860, Spain

*e-mail: hb.iiaat@gmail.com
**e-mail: nebylov@aanet.ru
***e-mail: hassensalhi@gmail.com
****e-mail: pau.closas@cttc.cat

Abstract

In this paper, description of modern direct filtering approaches in INS/GNSS integration are presented, Cubature Kalman Filter (CKF) and Gauss Hermite Kalman Filter (GHKF). Robust design of low cost IMU/GNSS is proposed to solve the problem of UAV attitude estimation problem based on multiple sensor fusion. Multiple GNSS antennas are assumed available to improve and increase the accuracy of satellite positioning system and observe attitude angles delivered by the IMU. Nonlinear approximation techniques such as Extended Kalman Filter (EKF), Sigma Point Kalman Filter (SPKF) and most recently developed algorithm Cubature Kalman Filter (CKF) with GHKF are tested in this work. Estimation accuracy compared with approximated Cramer Rao Lower Bound CRLB provided a classification of different nonlinear filters clearly deduced during multiple simulated state estimations.

Keywords:

GNSS, UAV, accelerometers, gyroscopes, Kalman filter, UKF, CDKF, CKF, GHKF

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