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Air Data Estimation by Fusing Navigation System and Flight Control System

Published online by Cambridge University Press:  30 April 2018

Chen Lu ,
Rong-Bing Li ,
Jian-Ye Liu  and
Ting-Wan Lei
Chen Lu*
Affiliation:
(Key Laboratory of Internet of Things and Control Technology in Jiangsu Province, Nanjing 20016, China) (Navigation Research Center, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)
Rong-Bing Li
Affiliation:
(Key Laboratory of Internet of Things and Control Technology in Jiangsu Province, Nanjing 20016, China) (Navigation Research Center, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)
Jian-Ye Liu
Affiliation:
(Key Laboratory of Internet of Things and Control Technology in Jiangsu Province, Nanjing 20016, China) (Navigation Research Center, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)
Ting-Wan Lei
Affiliation:
(Chengdu Aircraft Design and Research Institute, Chengdu 610000, China)
*
(E-mail: [email protected])
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Abstract

A novel synthetic air data estimation method without using air data sensors is presented, and the method only relies on the information from the Navigation System (NS) and Flight Control System (FCS). The aircraft's aerodynamic model is also required to make a connection between the FCS control parameters and the NS measurements. The airspeed, angle of attack and sideslip, angular velocity and wind speed are defined as state vectors, and state equations are established through the aircraft's aerodynamic model and dynamics. Linear velocity and angular velocity provided by the navigation system are considered as the measurement vector. To deal with variable wind fields, a novel Initialised Three-step Extended Kalman Filter (ITEKF), which considers the wind speed as an unknown input, is developed to track the variation of wind speed. Simulation results based on a Generic Hypersonic Vehicle (GHV) model are presented and compared with an existing method. Factors affecting the method's accuracy include the navigation system accuracy and the aerodynamic model error, are also discussed.

Keywords

Air data estimationNavigationKalman filterAngle of attackFlight controlAerodynamic model
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 5 , September 2018 , pp. 1231 - 1246
Copyright
Copyright © The Royal Institute of Navigation 2018 

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References

REFERENCES

Baumann, E., Pahle, J., Davis, M. and White, J. (2008). X-43A Flush Air Data Sensing System Flight-test Results. Journal of Spacecraft and Rockets, 47(1), 48–61.Google Scholar
Cho, A., Kim, J., Lee, S.and Kee, C. (2011). Wind Estimation and Airspeed Calibration Using a UAV with a Single-Antenna GPS Receiver and Pitot Tube. IEEE Transactions on Aerospace and Electronic Systems, 47(1), 109–117.Google Scholar
Chowdhary, G. and Jategaonkar, R. (2010). Aerodynamic Parameter Estimation from Flight Data Applying Extended and Unscented Kalman Filter. Aerospace Science and Technology, 14 (2), 106–117.Google Scholar
Etkin, B. (2000). Dynamics of Atmospheric Flight. Dover Publications, New York, US.Google Scholar
Fravolini, M.L., Pastorelli, M., Pagnottelli, S., Valigi, P., Gururajan, S., Chao, H.and Napolitano, M.R. (2012). Model-Based Approaches for the Airspeed Estimation and Fault Monitoring of an Unmanned Aerial Vehicle. IEEE Workshop on Environmental Energy & Structural Monitoring Systems (EESMS), 18–23.Google Scholar
Gillijns, S. and Moor, B.D. (2007). Unbiased Minimum-Variance Input and State Estimation for Linear Discrete-Time Systems with Direct Feedthrough. Automatica, 43, 934–937.Google Scholar
Guo, J.F., Fu, Y.and Cui, N.G. (2013). Three-Dimensional Autonomous Entry Guidance Method. Control and Decision, 28(5), 688–694.Google Scholar
Kampoon, J.W., Okolo, W., Erturk, S.A., Daskiran, O.and Dogan, A. (2015). Wind Field Estimation and Its Utilization in Trajectory Prediction. AIAA SciTech Forum, 1–26.Google Scholar
Kargaard, C.D., Kutty, P.and Schoenenberger, M. (2015). Coupled Inertial Navigation and Flush Air Data Sensing Algorithm for Atmosphere Estimation. AIAA Atmospheric Flight Mechanics Conference, AIAA SciTech Forum, 1–18.Google Scholar
Keshmiri, S. and Colgren, R. (2007). Six DoF Nonlinear Equations of Motion for a Generic Hypersonic Vehicle. AIAA Atmospheric Flight Mechanics Conference and Exhibit, 1–29.Google Scholar
Li, Q.D., Chen, L.L.and Zhang, X.G. (2009). Flush Airdata Sensing System Fast Intelligent Fault Detection and Diagnosis Technology. Systems Engineering and Electronics, 31(10), 2544–2546.Google Scholar
Lie, F.A.P. and Egziabher, D.G. (2013). Synthetic Air Data System. Journal of Aircraft, 50(4), 1234–1249.Google Scholar
Lu, P., Eykeren, L.V., Kampen, E.V., Visser, C.C.D. and Chu, Q.P. (2016). Adaptive Three-Step Kalman Filter for Air Data Sensor Fault Detection and Diagnosis. Journal of Guidance, Control, and Dynamics, 39(3), 590–604.Google Scholar
Lyv, P., Lai, J.Z., Liu, J.Y., Zhu, B.and Song, Y.F. (2015). Overview and Progress on Study of Aircraft Aerodynamics Model Aided Navigation Method. Control and Decision, 30(11), 1–7.Google Scholar
Myschik, S., Holzapfel, F.and Sachs, G. (2008). Low-Cost Sensor Based Integrated Airdata and Navigation System for General Aviation Aircraft. Proceedings of AIAA Guidance Navigation and Control Conference and Exhibit, 1–21.Google Scholar
Nebula, F., Palumbo, R.and Morani, G. (2013). Virtual Air Data: a Fault-Tolerant Approach Against ADS Failures. AIAA Infotech at Aerospace Conference. 1–14.Google Scholar
Rhudy, M., Larrabee, T., Chao, H.Y., Gu, Y.and Napolitano, M.R. (2013). UAV Attitude, Heading, and Wind Estimation Using GPS/INS and an Air Data System. AIAA Guidance, Navigation, and Control Conference, 1–11.Google Scholar
Rohlf, D., Brieger, O.and Grohs, T. (2004). X-31 VECTOR System Identification Approach and Results. AIAA Atmospheric Flight Mechanics Conference and Exhibit, 1–12.Google Scholar
Wang, Y.F., Wu, Q.X., Jiang, C.S. and Zhang, Q. (2012). Multi-model Switching Control for Near Space Vehicle. Control and Decision, 27(10), 1452–1458.Google Scholar
Westhelle, C.H. (2002). X-38 Backup Air Data System (AeroDAD). 40th AIAA Aerospace Sciences Meeting and Exhibit, 1–12.Google Scholar
Whitmore, S.A. (2002). Reconstruction of the Shuttle Reentry Air Data Parameters Using a Linearized Kalman Filter. AIAA Atmospheric Flight Mechanics Conference, 1–13.Google Scholar
Wise, K.A. (2013). Flight Testing of the X-45A J-UCAS Computational Alpha-Beta System. AIAA Guidance, Navigation, and Control Conference and Exhibit, 1–14.Google Scholar
Yadav, V., Padhi, R.and Balakrishnan, S.N. (2007). Robust/Optimal Temperature Profile Control of a High-Speed Aerospace Vehicle Using Neural Networks. IEEE Transactions on Neural Networks, 18(4), 1115–1128.Google Scholar
Zhang, C., Xiong, Z., Wang, R., Liu, J.Y. and Peng, H. (2013). New INS/CNS Integrated Algorithm On Aerospace Vehicle with Directly Sensing Horizon. Chinese Space Science and Technology, 3, 64–71.Google Scholar
Zeis, J.E. (1988). Angle-of-Attack and Sideslip Estimation Using Inertial Reference Platform. Air Force Institute of Technology, Wright-Patterson Air Force Base, 80–84.Google Scholar