A Method of Rescue Flight Path Plan Correction Based on the Fusion of Predicted Low-altitude Wind Data

  • Ming Zhang Nanjing University of Aeronautics and Astronautics
  • Shuo Wang Nanjing University of Aeronautics and Astronautics
  • Hui Yu Nanjing University of Aeronautics and Astronautics
Keywords: low-altitude rescue, flight path correction, meteorological prediction model, unscented Kalman filter,

Abstract

This study proposes a low-altitude wind prediction model for correcting the flight path plans of low-altitude aircraft. To solve large errors in numerical weather prediction (NWP) data and the inapplicability of high-altitude meteorological data to low altitude conditions, the model fuses the low-altitude lattice prediction data and the observation data of a specified ground international exchange station through the unscented Kalman filter (UKF)-based NWP interpretation technology to acquire the predicted low-altitude wind data. Subsequently, the model corrects the arrival times at the route points by combining the performance parameters of the aircraft according to the principle of velocity vector composition. Simulation experiment shows that the RMSEs of wind speed and direction acquired with the UKF prediction method are reduced by 12.88% and 17.50%, respectively, compared with the values obtained with the traditional Kalman filter prediction method. The proposed prediction model thus improves the accuracy of flight path planning in terms of time and space.

Author Biographies

Ming Zhang, Nanjing University of Aeronautics and Astronautics
Dr Ming Zhang is associate professor of Nanjing University of Aeronautics and Astronautics (Nanjing, China). As a visiting scholar of USF, he visit Dr. Zhang’s research group from Aug.2014 to Aug.2015.He obtained his B.E. in mechanical engineering in 1997 from NUAA. He obtained his master degree and Ph.D. in 2003 and 2010 from NUAA.His research interests focus on dynamic airspace configuration, airspace and airfield capacity and delay, and general aviation rescue system in air transportation.
Shuo Wang, Nanjing University of Aeronautics and Astronautics

References

Korn B, Helmke H, Kuenz A. 4D trajectory management in the extended TMA: coupling AMAN and 4D FMS for optimized approach trajectories. 25th ICAS; Hamburg, Germany; 2006.

Torres JL, Garcia A, De Blas M, et al. Forecast of hourly average wind speed with ARMA models in Navarre (Spain). Solar Energy. 2005;79(1):65-77.

Louka P, Galanis G, Siebert N, et al. Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering. Journal of Wind Engineering and Industrial Aerodynamics. 2008;96(12):2348-2362.

Chen K, Yu J. Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach. Applied Energy. 2014;113:690-705.

Tagliaferri F, Viola IM, Flay RGJ. Wind direction forecasting with artificial neural networks and support vector machines. Ocean Engineering. 2015;97:65-73.

Frehlich R, Sharman R. Climatology of velocity and temperature turbulence statistics determined from rawinsonde and ACARS/AMDAR data. Journal of Applied Meteorology and Climatology. 2010;49(6):1149-1169.

Fukuda Y, Shirakawa M, Senoguchi A. Development of Trajectory Prediction Model. Tokyo, Japan: ENRI International Workshop on ATM/CNS (EIWAC); 2010.

Hurter C, Alligier R, Gianazza D, et al. Wind parameters extraction from aircraft trajectories. Computers, Environment and Urban Systems, 2014;47:28-43.

Gariel M, Srivastava AN, Feron E. Trajectory clustering and an application to airspace monitoring. Intelligent Transportation Systems, IEEE Transactions on. 2011;12(4):1511-1524.

Lee AG, Weygandt SS, Schwartz B, et al. Performance of trajectory models with wind uncertainty. AIAA Modeling and Simulation Technologies Conference; Chicago, Illinois; 2009.

Zheng QM, Zhao JY. Modeling Wind Uncertainties for Stochastic Trajectory Synthesis. 11th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference; 2011 Sep 20-22; Virginia Beach, VA; 2011.

Lymperopoulos I, Lygeros J. Sequential Monte Carlo methods for multi-aircraft trajectory prediction in air traffic management. International Journal of Adaptive Control and Signal Processing. 2010;24(10):830-849.

Hu J, Prandini M, Sastry S. Aircraft conflict prediction in the presence of a spatially correlated wind field. Intelligent Transportation Systems, IEEE Transactions on. 2005;6(3):326-340.

Kandepu R, Foss B, Imsland L. Applying the unscented Kalman filter for nonlinear state estimation. Journal of Process Control. 2008;18(7):753-768.

Published
2016-10-26
How to Cite
1.
Zhang M, Wang S, Yu H. A Method of Rescue Flight Path Plan Correction Based on the Fusion of Predicted Low-altitude Wind Data. PROMET [Internet]. 2016Oct.26 [cited 2019Dec.13];28(5):479-85. Available from: http://traffic.fpz.hr/index.php/PROMTT/article/view/1939
Section
Articles