Determination of Wind Velocity Projections Taking into Account Measurements of Airspeed, Angles of Attack and Sideslip

The paper deals with the problem of estimating the projections of the wind velocity in flight. The proposed method allows to obtain estimates for three projections of wind speed in the normal Earth coordinate system using data from the satellite navigation system, as well as on-board aerometric measurements of airspeed, angles of attack and glide. The main idea underlying the method is that satellite measurements of three aircraft velocity projections relative to the Earth’s coordinate system are very accurate (errors usually do not exceed 0.2 m/s). This makes it possible to use satellite velocity measurements as a kind of reference, just as in practical metrology, in order to assess the errors of measurement tools, they are compared with a standard, that is, a significantly more accurate measurement tool. In order to implement this approach not in a metrological laboratory, but on board an aircraft, it is proposed to use the relationships known from the flight dynamics between the velocity projections in the Earth’s and associated coordinate systems, the angles of attack and glide, and the wind speed. Then, the three wind speed projections are assigned unknown parameters, which are found using parameter identification. It is assumed that the wind has a constant speed and direction in the processed section of the flight. The accuracy characteristics of the proposed algorithm were evaluated based on the data obtained on the flight simulator of a modern training aircraft. In the course of simulation, random measurement errors were generated at the levels corresponding to the flight experiment. The influence of the type of maneuvers on the accuracy the three wind speed projections estimates was also studied. It is shown that for all considered maneuvers, that is "barrel", "snake", stepwise inputs, the errors in estimating the horizontal components of wind speed generally do not exceed 5 %, the vertical component 10 %, with the duration of the sliding processing interval of 0.5 and 1.0 s, which allows not only to estimate the constant wind speed, but also to track its change.

1. Vasil’chenko K. K., Leonov V. A., Pashkovskij I. M., Poplavskij B. K. Aircraft flight tests, Moscow, Mashinostroenie,1996, 745 p.

2. Avgustov L. I., Babichenko A. V. et al. Aircraft navigation in near Earth space, Moscow, Nauchtekhlitizdatp 2015, 592 p. (in Russian)

3. Klein V., Morelli E. Aircraft System Identification. Theory and Practice, Reston, AIAA, 2006, 484 p.

4. Korsun O. N., Poplavsky B. K. Approaches for flight tests aircraft parameter identification, 29th Congress of the International Council of the Aeronautical Sciences, ICAS 2014, 2014, pp. 02—10.

5. Jategaonkar R. V. Flight vehicle system identification: A time domain methodology, Reston, AIAA, 2006, 534 p.

6. Ovcharenko V. N. Aircraft aerodynamic parameters: flight data identification, Moscow, LENAND, 2019, 236 p.

7. Chowdhary G., Jategaonkar R. Aerodynamic parameter estimation from flight data applying extended and unscented Kalman filter, Aerospace Science and Technology, 2010, vol. 14, pp. 106—117.

8. Brunton S. L., Dawson S. T. M., Rowley C. W. State-space model identification and feedback control of unsteady aerodynamic forces, Journal of Fluids and Structures, 2014, vol. 50, pp. 253—270.

9. Wang Y., Dong J., Liu X., Zhang L. Identification and standardization of maneuvers based upon operational flight data, Chinese Journal of Aeronautics, 2015, vol. 28, no. 1, pp. 133—140.

10. Song Y., Song B., Seanor B. et al. On-line aircraft parameter identification using Fourier transform regression with an application to F/A-18 HARV flight data, KSME International Journal, 2002, vol. 16, no. 3, pp. 327—337.

11. Boubertakh H. Knowledge-based ant colony optimization method to design fuzzy proportional integral derivative controllers // Journal of Computer and Systems Sciences International, 2017, vol. 56, iss. 4, pp. 681—700.

12. Luchtenburg D. M., Rowley C. M., Lohry M. W., Martinelli L., Stengel R. F. Unsteady high-angle-of-attack aerodynamic models of a generic jet transport, Journal of Aircraft, 2015, vol. 52, no. 3, pp. 890—895.

13. Wang Q., He K. F., Qian W. Q., Zhang T. J., Cheng Y. Q., Wu K. Y. Unsteady aerodynamics modeling for flight dynamics application, Acta Mechanica Sinica, 2012, vol. 28, no. 1, pp. 14—23.

14. Schutte A., Einarsson G., Raichle A., Schoning B., Monnich W., Forkert T. Numerical simulation of maneuvering aircraft by aerodynamic, flight mechanics, and structural mechanics coupling, J. Aircraft, 2009, vol. 46, no. 1, pp. 53—64.

15. Kanyshev A. V., Korsun O. N., Ovcharenko V. N., Stulovskii A. V. Identification of aerodynamic coefficients of longitudinal movement and error estimates for onboard measurements of supercritical angles of attack, Journal of Computer and System Science International, 2018, vol. 57, no. 3, pp. 374—389.

16. Korsun O. N., Nikolaev S. V., Pushkov S. G. An algorithm for estimating systematic measurement errors for air velocity, angle of attack, and sliding angle in flight testing, Journal of Computer and Systems Sciences International, 2016, vol. 55, no. 3, pp. 446—457.

17. Wagner J. F., Wieneke T. Integrating satellite and inertial navigation conventional and new fusion approaches, Control Engineering Practice, 2003, vol. 11, no. 5, pp. 483—598.

18. Pushkov S. G., Lovitsky L. L., Korsun O. N. Wind speed determination methods in flight tests using satellite navigation systems), Mekhatronika, Avtomatizatsiya, Upravlenie, 2013, no. 9, pp. 65—70 (in Russian).

19. Kyaw Zin Latt, Moung Htang Om. Development of wind velocity estimation method using the airspeed, Vestnik Moskovskogo aviacionnogo institute, 2018, vol. 25, no. 2, pp. 152—159.

20. Efremov A. V., Zaharchenko V. F., Ovcharenko V. N. et al. Dinamika poleta: Dynamics of Flight, Moscow, Mashinostroenie, 2017, 776 p. (in Russian).