Air Data Estimation Algorithm under Unknown Wind Based on Information Fusion
Publication: Journal of Aerospace Engineering
Volume 31, Issue 5
Abstract
Physical pressure sensors installed on a vehicle’s surface are the general way to find air data, such as true airspeed, attack angle, and sideslip angle. Under extreme flight conditions, failure of pressure measurements are a possibility. Estimating air data based only on navigation information and flight control parameters is a potential method for providing a backup virtual air data system (VADS). Ordinarily, wind velocity is assumed to be known in VADS. To solve the air data estimation problem without initial wind velocity, we propose air data estimation algorithms with and without wind models. We used kinematics equations and aerodynamic models to establish the relationship between navigation information and wind velocity. We estimated wind speed using nonlinear filtering algorithms, then obtained air data parameters. We ran simulation experiments with the proposed estimation algorithms, and the results show that the proposed method achieves higher convergence speed and estimation accuracy.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant No. 61374115) and the key project of the National Natural Science Foundation of China (Grant No. 61533008).
References
Beard, R., and T. McLain. 2012. Small unmanned aircraft: Theory and practice. Princeton, NJ: Princeton University Press.
Cho, A., J. Kim, S. Lee, and C. Kee. 2011. “Wind estimation and airspeed calibration using a UAV with a single-antenna GPS receiver and Pitot tube.” IEEE Trans. Aerosp. Electron. Syst. 47 (1): 109–117. https://doi.org/10.1109/TAES.2011.5705663.
Chowdhary, G., and R. Jategaonkar. 2010. “Aerodynamic parameter estimation from flight data applying extended and unscented Kalman filter.” Aerosp. Sci. Technol. 14 (2): 106–117. https://doi.org/10.1016/j.ast.2009.10.003.
Etkin, B. 2000. Dynamics of atmospheric flight. New York: Dover Publications.
Fravolini, M. L., M. Pastorelli, S. Pagnottelli, and P. Valigi. 2012. “Model-based approaches for the airspeed estimation and fault monitoring of an unmanned aerial vehicle.” In Proc., IEEE Workshop on Environmental Energy and Structural Monitoring Systems (EESMS), 18–23. Italy: IEEE.
Gillijns, S., and B. D. Moor. 2007. “Unbiased minimum-variance input and state estimation for linear discrete-time systems with direct feedthrough.” Automatica 43 (5): 934–937. https://doi.org/10.1016/j.automatica.2006.11.016.
Karlgaard, C. D., P. Kutty, and M. Schoenenberger. 2016. “Coupled inertial navigation and flush air data sensing algorithm for atmosphere estimation.” J. Spacecraft Rockets 54 (1): 128–140. https://doi.org/10.2514/1.A33331.
Keshmiri, S., and R. Colgren. 2005. “Development of an aerodynamic database for a generic hypersonic air vehicle.” In Proc., AIAA Guidance, Navigation, and Control Conf. and Exhibit. Reston, VA: American Institute of Aeronautics and Astronautics.
Li, Q. C., J. F. Liu, X. Liu, Y. C. Yang, J. Z. Chen, P. Z. Tian, J. Yang, H. B. Kang, and S. M. Zhu. 2014. “The primary study of 3-point calculation method for the flush air data system.” Acta Aerodyn. Sin. 32 (3): 360–363.
Lie, F. A. P., and D. Gebre-Egziabher. 2013. “Synthetic air data system.” J. Aircr. 50 (4): 1234–1249. https://doi.org/10.2514/1.C032177.
Lu, P., J. Z. Lai, J. Y. Liu, B. Zhu, and Y. F. Song. 2015. “Overview and progress on study of aircraft aerodynamics model aided navigation method.” Control Decis. 30 (11): 1–7.
Myschik, S., F. Holzapfel, and G. Sachs. 2008. “Low-cost sensor based integrated air data and navigation system for general aviation aircraft.” In Proc., AIAA Guidance Navigation and Control Conf. and Exhibit, 2008–7423. Reston, VA: American Institute of Aeronautics and Astronautics.
Nebula, F., R. Palumbo, and G. Morani. 2013. “Virtual air data: A fault-tolerant approach against ADS failures.” In Proc., AIAA Infotech at Aerospace Conf., 2013–4568. Reston, VA: American Institute of Aeronautics and Astronautics.
Nebula, F., R. Palumbo, G. Morani, and F. Corraro. 2009. “Virtual air data system architecture for space reentry applications.” J. Spacecraft Rockets 46 (4): 818–828. https://doi.org/10.2514/1.42485.
Rhudy, M., Y. Gu, J. Gross, and H. Y. Chao. 2017. “Onboard wind velocity estimation comparison for unmanned aircraft systems.” IEEE Trans. Aerosp. Electron. 53 (1): 55–66. https://doi.org/10.1109/TAES.2017.2649218.
Rhudy, M., T. Larrabee, H. Y. Chao, Y. Gu, and M. R. Napolitano. 2013. “UAV attitude, heading, and wind estimation using GPS/INS and an air data system.” In Proc., AIAA Guidance Navigation and Control Conf. and Exhibit, 2013–5201. Reston, VA: American Institute of Aeronautics and Astronautics.
Wenz, A., T. A. Johansen, and A. Cristofaro. 2016. “Model-free and model-based angle of attack estimation for small fixed-wing UAVs using a standard sensor suite.” In Proc., Int. Conf. on Unmanned Aircraft Systems, 624–632. Piscataway, NJ: IEEE.
Wise, K. A. 2006. “Flight testing of the X-45A J-UCAs computational A-B system.” In Proc., AIAA Guidance Navigation and Control Conf. and Exhibit, 2006–6215. Reston, VA: American Institute of Aeronautics and Astronautics.
Zeis, J. E. 1988. “Angle-of-attack and sideslip estimation using inertial reference platform.” Ph.D. thesis, Air Force Institute of Technology, Wright-Patterson Air Force Base.
Zhou, G. C., Q. D. Li, and Y. M. Guo. 2014. “A highly precise flush air-data sensing system algorithm.” J. Northwestern Polytech. Univ. 32 (3): 351–355.
Information & Authors
Information
Published In
Copyright
©2018 American Society of Civil Engineers.
History
Received: Jul 21, 2017
Accepted: Mar 7, 2018
Published online: Jun 27, 2018
Published in print: Sep 1, 2018
Discussion open until: Nov 27, 2018
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.