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CLC number: TP391

On-line Access: 2018-09-04

Received: 2017-04-05

Revision Accepted: 2017-05-22

Crosschecked: 2018-07-08

Cited: 0

Clicked: 1657

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Yi Lin

http://orcid.org/0000-0002-7194-5023

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Frontiers of Information Technology & Electronic Engineering  2018 Vol.19 No.7 P.905-916

10.1631/FITEE.1700224


An algorithm for trajectory prediction of flight plan based on relative motion between positions


Author(s):  Yi Lin, Jian-wei Zhang, Hong Liu

Affiliation(s):  National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu 610065, China; more

Corresponding email(s):   scu_lyi@stu.scu.edu.cn, liuhong@scu.edu.cn

Key Words:  Velocity vector, Hidden Markov model, Gaussian mixture model, Machine learning, Plan path prediction, Relative motion between positions (RMBP)


Yi Lin, Jian-wei Zhang, Hong Liu. An algorithm for trajectory prediction of flight plan based on relative motion between positions[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(7): 905-916.

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Abstract: 
Traditional methods for plan path prediction have low accuracy and stability. In this paper, we propose a novel approach for plan path prediction based on relative motion between positions (RMBP) by mining historical flight trajectories. A probability statistical model is introduced to model the stochastic factors during the whole flight process. The model object is the sequence of velocity vectors in the three-dimensional Earth space. First, we model the moving trend of aircraft including the speed (constant, acceleration, or deceleration), yaw (left, right, or straight), and pitch (climb, descent, or cruise) using a hidden Markov model (HMM) under the restrictions of aircraft performance parameters. Then, several gaussian mixture models (GMMs) are used to describe the conditional distribution of each moving trend. Once the models are built, machine learning algorithms are applied to obtain the optimal parameters of the model from the historical training data. After completing the learning process, the velocity vector sequence of the flight is predicted by the proposed model under the Bayesian framework, so that we can use kinematic equations, depending on the moving patterns, to calculate the flight position at every radar acquisition cycle. To obtain higher prediction accuracy, a uniform interpolation method is used to correct the predicted position each second. Finally, a plan trajectory is concatenated by the predicted discrete points. Results of simulations with collected data demonstrate that this approach not only fulfils the goals of traditional methods, such as the prediction of fly-over time and altitude of waypoints along the planned route, but also can be used to plan a complete path for an aircraft with high accuracy. Experiments are conducted to demonstrate the superiority of this approach to some existing methods.

基于飞行相对运动的航班计划轨迹预测算法

概要:传统航班轨迹预测算法精度低,稳定性差。通过挖掘历史飞行轨迹,提出一种新的基于相邻位置相对运动的航班计划轨迹预测算法。引入概率统计模型,并基于三维速度矢量,对航班运行过程中的随机特征进行建模。在飞机性能参数限制下,基于隐马尔科夫模型对航班运动趋势建模,包含速度(匀速、匀加速、匀减速),航向(左转、右转、直行),和俯仰(上升、下降、平飞)。采用高斯混合模型描述每种运动趋势下飞行参数的条件概率分布,并基于历史雷达数据优化该模型的相关参数。在预测阶段,该模型在贝叶斯框架下预测航班飞行的速度矢量序列,并基于运动学模型计算每个雷达更新周期的航班轨迹。为提高预测结果的准确性,采用均匀插值算法,以一秒为间隔校正预测的航班位置,最终形成完整的航班计划轨迹。基于真实采集数据的仿真结果表明,该算法不仅能准确预测航班航路关键点的时间和高度,还能以较高精度预测航班在飞行过程中的完整轨迹。相对于已有轨迹预测算法,提出的算法有更高预测精度和更好稳定性。

关键词:速度矢量;隐马尔科夫模型;高斯混合模型;机器学习;航班轨迹预测;相邻位置相对运动

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Reference

[1]Alligier R, Gianazza D, Durand N, 2015. Machine learning and mass estimation method for ground-based aircraft climb prediction. IEEE Trans Intell Transp Syst, 16(6): 3138-3149.

[2]Ayhan S, Samet H, 2016. Aircraft trajectory prediction made easy with predictive analytics. ACM SIGKDD Int Conf on Knowledge Discovery & Data Mining, p.21-30.

[3]Barrios C, Motai Y, 2011. Improving estimation of vehicle’s trajectory using the latest global positioning system with Kalman filtering. IEEE Trans Instrum Meas, 60(12): 3747-3755.

[4]Chen ZJ, 2010. Theory and Method of Airspace Management. Science Press, Beijing, China, p.217-227 (in Chinese).

[5]Ding ZM, Yang B, Güting RH, et al., 2015. Network-matched trajectory-based moving-object database: models and applications. IEEE Trans Intell Transp Syst, 16(4): 1918-1928.

[6]Gardi A, Sabatini R, Ramasamy S, et al., 2013. 4-Dimensional trajectory negotiation and validation system for the next generation air traffic management. AIAA Guidance, Navigation, and Control Conf, p.1-15.

[7]Hamed MG, Gianazza D, Serrurier M, et al., 2013. Statistical prediction of aircraft trajectory: regression methods vs point-mass model. 10th USA/Europe Air Traffic Management Research and Development Seminar, p.1-11.

[8]Jeung HY, Shen HT, Zhou XF, 2007. Mining trajectory patterns using hidden Markov models. Int Conf on Data Warehousing and Knowledge Discovery, p.470-480.

[9]Li Z, Li SH, Wu XL, 2015. General aircraft 4D flight trajectory prediction method based on data fusion. Int Conf on Machine Learning and Cybernetics, p.309-315.

[10]Lymperopoulos I, Lygeros J, 2010. Sequential Monte Carlo methods for multi-aircraft trajectory prediction in air traffic management. Int J Adapt Contr Signal Process, 24(10):830-849.

[11]Mahler PSR, 2011. Statistical Multisource-Multitarget Information Fusion. National Defense Industry Press, Beijing, China, p.27-37 (in Chinese).

[12]Morzy M, 2007. Mining frequent trajectories of moving objects for location prediction. Proc 5th Int Conf on Machine Learning and Data Mining in Pattern Recognition, p.667-680.

[13]Naseri A, Neogi N, Rantanen E, 2007. Stockastic hybrid models with applications to air traffic management. AIAA Guidance, Navigation, and Control Conf and Exhibit, p.370-379.

[14]Prento T, Thom A, Blunck H, et al., 2015. Making sense of trajectory data in indoor spaces. IEEE Int Conf on Mobile Data Management, p.9424-9436.

[15]Qiao MY, Bian W, Xu RYD, et al., 2015. Diversified hidden Markov models for sequential labeling. IEEE Trans Knowl Data Eng, 27(11):2947-2960.

[16]Qiao SJ, Jin K, Han N, et al., 2015a. Trajectory prediction algorithm based on Gaussian mixture model. J Softw, 26(5):1048-1063.

[17]Qiao SJ, Shen DY, Wang XT, et al., 2015b. A self-adaptive parameter selection trajectory prediction approach via hidden Markov models. IEEE Trans Intell Transp Syst, 16(1):284-296.

[18]Shanmuganathan SK, 2014. A HMM-Based Prediction Model for Spatio-Temporal Trajectories. MS Thesis, the University of Texas at Arlington, Dallas, USA.

[19]Song LL, 2012. A 4-D trajectory prediction method based on set of historical trajectory. Comput Technol Dev, 12:11-14.

[20]Tang KS, Zhu SF, Xu YQ, et al., 2016. Modeling drivers’ dynamic decision-making behavior during the phase transition period: an analytical approach based on hidden Markov model theory. IEEE Trans Intell Transp Syst, 17(1):206-214.

[21]Tang XM, Chen P, Zhang Y, 2015a. 4D trajectory estimation based on nominal flight profile extraction and airway meteorological forecast revision. Aerosp Sci Technol, 45:387-397.

[22]Tang XM, Gu JW, Shen ZY, et al., 2015b. A flight profile clustering method combining TWED with K-means algorithm for 4D trajectory prediction. Integrated Communication, Navigation, and Surveillance Conf, p.1-9.

[23]Tang XM, Zhou L, Shen ZY, et al., 2015c. 4D trajectory prediction of aircraft taxiing based on fitting velocity profile. 15th COTA Int Conf of Transportation Professionals, p.1-12.

[24]Wandelt S, Sun XQ, 2015. Efficient compression of 4D-trajectory data in air traffic management. IEEE Trans Intell Transp Syst, 16(2):844-853.

[25]Xie AM, Cheng P, 2015. 4D approaching trajectory design in terminal area based on radar data. Appl Mech Mater, 740:731-735.

[26]Yepes JL, Hwang I, Rotea M, 2007. New algorithms for aircraft intent inference and trajectory prediction. J Guid Contr Dynam, 30(2):370-382.

[27]Zahariand A, Jaafar J, 2015. Combining hidden Markov model and case based reasoning for time series forecasting. Commun Comput Inform Sci, 513:237-247.

[28]Zhang JF, Jiang HX, Wu XG, 2014. 4D trajectory prediction based on BADA and aircraft intent. J Southwest Jiaotong Univ, 49(3):553-558.

[29]Zheng Y, Zhou XF, 2012. Computing with Spatial Trajectories. Springer, New York, USA, p.337-394.

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