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论文:2019,Vol:37,Issue(3):612-620 |
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引用本文： |
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王新, 杨任农, 左家亮, 徐西蒙, 岳龙飞. 基于HPSO-TPFENN的目标机轨迹预测[J]. 西北工业大学学报 |
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WANG Xin, YANG Rennong, ZUO Jialiang, XU Ximeng, YUE Longfei. Trajectory Prediction of Target Aircraft Based on HPSO-TPFENN Neural Network[J]. Northwestern polytechnical university |
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| 基于HPSO-TPFENN的目标机轨迹预测 |
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| 王新, 杨任农, 左家亮, 徐西蒙, 岳龙飞 |
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| 空军工程大学 空管领航学院, 陕西 西安 710051 |
| 摘要: |
| 轨迹预测在现代空战中发挥着重要作用。针对传统轨迹预测模型复杂度大，预测精度偏低，数据样本真实性、可靠性差等问题，结合轨迹数据具有时间连续性的特点，提出了基于HPSO-TPFENN的轨迹预测模型。在Elman神经网络的目标函数中引入时间收益因子，并利用杂交粒子群算法（HPSO）对改进的Elman网络进行参数寻优，构造了HPSO-TPFENN神经网络。提出将三维坐标进行独立预测的方法，并根据空战训练测量仪（ACMI）中记录的真实数据，构建了包括航向角和俯仰角在内的轨迹预测数据样本，建立了基于HPSO-TPFENN的轨迹预测模型。通过仿真实验对比分析了模型进行轨迹预测的精度和实时性，结果表明模型在不同方向的预测误差不超过1%，连续进行595次预测耗时42 ms左右，可以准确、快速地对目标机的轨迹进行预测。 |
| 关键词:
轨迹预测
时间收益因子
HPSO-TPFENN
空战训练测量仪
独立预测
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| Trajectory Prediction of Target Aircraft Based on HPSO-TPFENN Neural Network |
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| WANG Xin, YANG Rennong, ZUO Jialiang, XU Ximeng, YUE Longfei |
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| Air Traffic and Navigation College, Air Force Engineering University, Xi'an 710051, China |
| Abstract: |
| 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
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| 收稿日期: 2018-06-25
修回日期:
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| DOI: 10.1051/jnwpu/20193730612 |
| 通讯作者: 左家亮(1986-),空军工程大学讲师,主要从事空战研究。E-mail:jialnzuo@163.com
Email：jialnzuo@163.com |
| 作者简介: 王新(1995-),空军工程大学硕士研究生,主要从事智能空战、态势评估研究。
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