A Method of Rescue Flight Path Plan Correction Based on the Fusion of Predicted Low-altitude Wind Data
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.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.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
