基于循环神经网络的目标转弯机动类型识别

吴家湖; 熊华; 宗睿; 赵曜; 周贤中

doi:10.12052/gdutxb.190074

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基于循环神经网络的目标转弯机动类型识别

综合研究 | 更新时间：2026-05-27

- 基于循环神经网络的目标转弯机动类型识别
- Target Turning Maneuver Type Recognition Based on Recurrent Neural Networks
- 广东工业大学学报 2020年37卷第2期页码：67-73
- 作者机构：
  
  1. 广东工业大学信息工程学院,广东,广州,510006
  2. 北京电子工程总体研究所,北京,100854
- 作者简介：
  
  吴家湖(1993–)，男，硕士研究生，主要研究方向为雷达跟踪滤波算法、图像处理、机器学习等
  赵曜(1984–)，男，副教授，硕士生导师，主要研究方向为雷达系统设计、合成孔径雷达信号处理、雷达跟踪滤波算法、稀疏信号处理等，E-mail：zhaoyao431@163.com
- 基金信息：
  
  国家自然科学基金青年基金资助项目(61704032);中国科学院计算技术研究所计算机体系结构国家重点实验室开放课题(CARCH201814)
- DOI：10.12052/gdutxb.190074
  中图分类号： TP183
- 网络首发：2020-03-10，
  
  纸质出版：2020
- 稿件说明：
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吴家湖, 熊华, 宗睿, 赵曜, 周贤中. 基于循环神经网络的目标转弯机动类型识别[J]. 广东工业大学学报, 2020, 37(2): 67-73.

Wu Jia-hu, Xiong Hua, Zong Rui, et al. Target Turning Maneuver Type Recognition Based on Recurrent Neural Networks[J]. 2020, 37(2): 67-73.
吴家湖, 熊华, 宗睿, 赵曜, 周贤中. 基于循环神经网络的目标转弯机动类型识别[J]. 广东工业大学学报, 2020, 37(2): 67-73. DOI： 10.12052/gdutxb.190074.

Wu Jia-hu, Xiong Hua, Zong Rui, et al. Target Turning Maneuver Type Recognition Based on Recurrent Neural Networks[J]. 2020, 37(2): 67-73. DOI： 10.12052/gdutxb.190074.

摘要

对战斗机等空中目标机动类型进行识别是掌握其战术意图的重要依据。为了更好地识别对方战机的机动类型

提升作战能力

本文主要研究空中高速运动目标(如战斗机)的机动类型识别

针对传统的机动类型识别算法的识别准确率不高的问题

将循环神经网络运用在机动类型的识别上

利用Bi-LSTM、LSTM、RNN和GRU循环神经网络识别转弯机动类型。实验结果表明

循环神经网络能够高效识别目标的转弯机动类型

Bi-LSTM的识别准确率达到了98.85%。

Abstract

Recognizing the maneuver types of airborne objects

such as fighters

is an important identification basis in figuring out their military intentions. To better identify the performance of maneuver types of enemy warfare aircraft and thus enhance our military's combat capability

the airborne high-speed moving targets such as the fighter's maneuver types are mainly studied. Addressing the low identification accuracy of traditional methods

recurrent neural networks are adopted to the problem of maneuver types recognition and identification. The main contribution of this study is the application of four kinds of recurrent neural networks architectures: Bi-LSTM (Bidirectional LSTM)

LSTM

RNN and GRU. The experiment results show that the proposed RNN based method can recognize the target's turning maneuver type with efficiency

and the identification accuracy of Bi-LSTM is achieved by 98.85%.

关键词

Keywords

references

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HUENHAGEN T, DENGLER I, TAMKE A, et al. Maneuver recognition using probabilistic finite-state machines and fuzzy logic[C]//Proceedings of the IEEE Conference on Intelligent Vehicles Symp. (IV), La Jolla, CA: IEEE, 2010, 65-70.

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GREFF K, SRIVASTAVA R K, KOUTNIK J, et al . LSTM: A search space odyssey [J ] . IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(10): 2222-2232.

GRAVES A, FERNANDEZ S, SCHMI-DHUBER J. Bidirectional LSTM networks for improved phoneme classification and recognition[C]//Proceedings of the International Conference on Artificial Neural Networks (ICANN). Berlin, Heidelberg: Springer, 2005: 799-804.

CHUNG J, GALCEHRE C, CHO K H, et al . Empirical evaluation of gated recurrent neural networks on sequence modeling [J ] . Eprint Arxiv, 2014, 11: 1-10.

何友, 修建娟, 关欣. 雷达数据处理及应用[M]. 北京: 电子工业出版社, 2013: 60-70.

周宏仁, 敬忠良, 王培德. 机动目标跟踪[M]. 北京: 国防工业出版, 1991: 78-108.

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