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

Yi LIN; Jian-wei ZHANG; Hong LIU

doi:10.1631/FITEE.1700224

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

Regular Papers | Updated：2022-05-19

- An algorithm for trajectory prediction of flight plan based on relative motion between positions
- 基于飞行相对运动的航班计划轨迹预测算法
- Frontiers of Information Technology & Electronic Engineering Vol. 19, Issue 7, Pages: 905-916(2018)
- Affiliations：
  
  National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu 610065, China
  National Key Laboratory of Air Traffic Control Automation System Technology, Sichuan University, Chengdu 610065, China
- Author bio：
  
  [ "LIN, E-mail: scu_lyi@stu.scu.edu.cn" ]
  Hong LIU, E-mail: liuhong@scu.edu.cn
- Funds：
- DOI：10.1631/FITEE.1700224
  CLC： TP391
- Received：05 April 2017，
  
  Revised：2018-;7-;8，
  
  Published：2018-07
- Accepted：
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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.
DOI：

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. DOI： 10.1631/FITEE.1700224.

摘要

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。

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.

关键词

Keywords

references

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