Short-term 4D trajectory prediction based on KF joint EKF parameter identification

• Civil Aviation • Next Articles

Short-term 4D trajectory prediction based on KF joint EKF parameter identification

ZHANG Tao¹, GAO Yang¹, ZHANG Chengwei², WU Renbiao¹

(1. Intelligent Signal and Image Processing Key Lab of Tianjin, CAUC, Tianjin 300300, China;2. Shenzhen Air Traffic Management Station, Shenzhen 512128, China)

Received:2015-11-10 Revised:2016-01-07 Online:2016-10-19 Published:2016-12-06

Abstract

Abstract:

A method based on KF-EKF jointed algorithm is proposed to identify motion model parameters for 4D trajectory prediction. On the basis of isometric track flight model, KF-EKF jointed algorithm is employed to identify the ground speed of aircraft, then the arriving time of aircraft爷s scheduled position is calculated. Latitude and longitude of aircraft are taken as observed variables and are updated respectively. Simulation result shows that it could reduce complexity of the computation and accurately predict the short-term flight track of constant speed cruise phase.

Key words: Kalman filter, extended Kalman filter, parameter identification, isometric track, 4D trajectory prediction

CLC Number:

TN959.4

ZHANG Tao, GAO Yang, ZHANG Chengwei, WU Renbiao. Short-term 4D trajectory prediction based on KF joint EKF parameter identification[J]. .

References 13

[1]	SOLOMOS G, URLASS S, BHADRA D, et al. Capacity Needs in the National Airspace System: An Analysis of Airport and Metropolitan Area Demand and Operational Capacity in the Future[R]. The MITRE Center for Advanced Aviation System Development, 2004.
[2]	卢奕羽. 初始飞行计划4D 航迹生成及仿真系统研究[D]. 南京:南京航空航天大学, 2014.
[3]	伊群. 美国新一代空中交通管理系统运行概念[J]. 中国民用航空,2007, 80(8): 27-31.
[4]	吕小平.中国民航新一代空中交通管理系统发展总体框架[J]. 中国民用航空, 2007, 80(8): 24-26.
[5]	PREVOST C G, DESBIENS A, GAGNON E. Extended Kalman Filter for State Estimation and Trajectory Prediction of a Moving Object Detected by an Unmanned Aerial Vehicle[C]//AmericanControlConference,NewYork,IEEE, 2007: 1805-1810.
[6]	LYMPEROPOULOS I, LYGEROS J. Sequential Monte Carlo methods for multi-aircraft trajectory prediction in air traffic management[J]. International Journal of Adaptive Control and Signal Processing, 2010, 24(10): 830-849.
[7]	吴鹍,潘薇.基于数据挖掘的四维飞行轨迹预测模型[J]. 计算机应用, 2007, 27(11): 2637-2639.
[8]	SLATTERY R, ZHAO Y. Trajectory synthesis for air traffic automation[J]. Journal of Guidance, Control and Dynamics, 1997, 20(2): 232-238.
[9]	王超,郭九霞,沈志鹏.基于基本飞行模型的4D航迹预测方法[J].西南交通大学学报, 2009, 44(2): 295-300.
[10]	CHESTERG,WILLIAMNC. Using Flight Manual Data toDeriveAero-Propulsive Models for Predicting Aircraft Trajectories[C]//AIAA Aircraft Technology, Integration and Operations (ATIO) 2002 Technical. California: AIAA, 2002: 1-7.
[11]	徐琴, 汤新民, 韩松臣,等.基于参数辨识的短期4D 航迹预测[J].信息与控制, 2014, 43(4): 501-505.
[12]	杨宏,李亚安, 李国辉. 一种改进扩展卡尔曼滤波新方法[J]. 计算机工程与应用, 2010, 46(19): 18-20.
[13]	吴晓光, 张军峰,蒋海行. GRIB 数据及其在航迹预测中的应用[J].航空计算技术, 2013, 43(6): 59-62.