Volume 37, Number 3, June 2019
|Page(s)||612 - 620|
|Published online||20 September 2019|
Trajectory Prediction of Target Aircraft Based on HPSO-TPFENN Neural Network
Air Traffic and Navigation College, Air Force Engineering University, Xi'an 710051, China
Trajectory prediction plays an important role in modern air combat. Aiming at the large degree of modern simplification, low prediction accuracy, poor authenticity and reliability of data sample in traditional methods, a trajectory prediction method based on HPSO-TPFENN neural network is established by combining with the characteristics of trajectory with time continuity. The time profit factor was introduced into the target function of Elman neural network, and the parameters of improved Elman neural network are optimized by using the hybrid particle swarm optimization algorithm (HPSO), and the HPSO-TPFENN neural network was constructed. An independent prediction method of three-dimensional coordinates is firstly proposed, and the trajectory prediction data sample including course angle and pitch angle is constructed by using true combat data selected in the air combat maneuvering instrument (ACMI), and the trajectory prediction model based on HPSO-TPFENN neural network is established. The precision and real-time performance of trajectory prediction model are analyzed through the simulation experiment, and the results show that the relative error in different direction is below 1%, and it takes about 42ms approximately to complete 595 consecutive prediction, indicating that the present model can accurately and quickly predict the trajectory of the target aircraft.
Key words: trajectory prediction / time profit factor / HPSO-TPFENN neural network / ACMI / independent prediction
关键字 : 轨迹预测 / 时间收益因子 / HPSO-TPFENN / 空战训练测量仪 / 独立预测
© 2019 Journal of Northwestern Polytechnical University. All rights reserved.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.