Data Fusion Algorithm for the Altitude and Vertical Speed Estimation of the VTOL Platform
An approach to the autonomous of the realization VTOL platform take-off and landing significantly simplifies the operator labor to control such device. At the same time, implementation of these control scenarios allows to perform these tasks under failure conditions (for example communication breakdowns). One condition of proper operation of the vertical movement control system is the ability to provide reliable information about the altitude of the controlled platform. In this paper one of the solutions for obtaining estimate of the altitude based on sensor data fusion is presented. Proposed scheme uses information obtained from pressure sensor, inertial measurement unit, ultrasonic sensor and GPS, all of these instruments are nowadays very often mounted on VTOL platforms.
KeywordsAltitude measurement Data fusion Complementary filter Kalman filter
Unable to display preview. Download preview PDF.
- 1.Yong, H., Ziyang, Z., Zhisheng, W.: Federated filter based multi-sensor fault-tolerant altitude determination system for UAV title. In: Proc. of Chinese Control and Decision Conference, pp. 2030–2034 (2008)Google Scholar
- 3.Wuhrl, T.: Safety problems of micro size unmanned air vehicles—onboard data fusion. AARMS 6(3), 491–501 (2007)Google Scholar
- 5.Bristeau, P.J., Callou, F., Vissiere, D.: The Navigation and control technology inside the AR.Drone micro UAV. In: Preprints of the 18th IFAC World Congress, pp. 1477–1484 (2011)Google Scholar
- 6.Bank, D.: An error detection model for ultrasonic sensor evaluation on autonomous mobile systems. In: 11th IEEE International Workshop on Robot and Human Interactive Communication, 2002. Proceedings, pp. 288–293 (2002)Google Scholar
- 7.Cherian, A., Andersh, J., Morellas, V., Papanikolopoulos, N., Mettler, B.: Autonomous altitude estimation of a UAV using a single onboard camera. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3900–3905 (2009)Google Scholar
- 8.Euston, M., Coote, P., Mahony, R., Kim, J., Hamel, T.: A complementary filter for attitude estimation of a fixed-wing UAV. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008. IROS 2008, pp. 340–345 (2008)Google Scholar
- 9.Tae, S.Y., Sung, K.H., Hyok, M.Y., Sungsu, P.: Gain-scheduled complementary filter design for a MEMS based attitude and heading reference system. Sensors 11(11), 3816–3830 (2011)Google Scholar
- 12.Crassidis, J.L., Junkins, J.L.: Optimal Estimation of Dynamic Systems. Chapman and Hall/CRC (2011)Google Scholar
- 13.Simon, D.: Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches. Wiley-Interscience (2006)Google Scholar
- 14.Yurkevich, V.D.: Design of Nonlinear Control Systems with the Highest Derivative in Feedback. World Scientific Publishing Co. (2004)Google Scholar