Journal of Guangdong University of Technology ›› 2020, Vol. 37 ›› Issue (02): 67-73.doi: 10.12052/gdutxb.190074

• Comprehensive Studies • Previous Articles     Next Articles

Target Turning Maneuver Type Recognition Based on Recurrent Neural Networks

Wu Jia-hu1, Xiong Hua2, Zong Rui2, Zhao Yao1, Zhou Xian-zhong1   

  1. 1. School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China;
    2. Beijing Institute of Electronic System Engineering, Beijing 100854, China
  • Received:2019-05-25 Online:2020-03-10 Published:2020-01-13

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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%.

Key words: maneuvering target, maneuver type, recurrent neural networks, Bi-LSTM (Bidirectional LSTM), maneuver recognition

CLC Number: 

  • TP183
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