Journal of Systems Engineering and Electronics ›› 2021, Vol. 32 ›› Issue (2): 498-516.doi: 10.23919/JSEE.2021.000042

• CONTROL THEORY AND APPLICATION • Previous Articles    

Target maneuver trajectory prediction based on RBF neural network optimized by hybrid algorithm

Zhifei XI*(), An XU, Yingxin KOU, Zhanwu LI, Aiwu YANG   

  1. 1 Aeronautics Engineering College, Air Force Engineering University, Xi’an 710038, China
  • Received:2020-03-18 Online:2021-04-29 Published:2021-04-29
  • Contact: Zhifei XI E-mail:18149365256@163.com
  • About author:|XI Zhifeiwas born in 1996. He graduated from the Aeronautics Engineering College, Air Force Engineering University in 2018. He is currently a master student in the same university. His research mainly focuses on air combat, machine intelligent and artificial intelligence. E-mail: 18149365256@163.com||XU An was born in 1984. He received his M.S. and Ph.D. degrees from Air Force Engineering University, Xi’an, China, in 2009 and 2012, respectively. He is currently a postdocotoral researcher at the Electronic Information College of Northwestern Polytechnical University, Xi’an, China, as well as a researcher with the Aeronautics Engineering College of Air Force Engineering University. His research interests include nonlinear and adaptive control, artificial intelligence and pattern recognition. E-mail: 18157494594@163.com||KOU Yingxin was born in 1965. He received his M.S. and Ph.D. degrees from Air Force Engineering University, Xi’an, China, in 1997 and 2010, respectively. He is currently a professor with the Aeronautics Engineering College of Air Force Engineering University. His research interests include air combat, nonlinear and adaptive control, artificial intelligence, multi-sensor data fusion, and optimized target assignment. E-mail: kgykyx@hotmail.com||LI Zhanwu was born in 1978. He received his M.S. degree from Air Force Engineering University, Xi’an, China, in 2007. He is currently an associate professor with the Aeronautics Engineering College of Air Force Engineering University. His research mainly focuses on multi-sensor data fusion, machine intelligent and artificial intelligence. E-mail: afeulzw@189.cn||YANG Aiwu was born in 1996. He graduated from the Aeronautics Engineering College of Air Force Engineering University in 2018. He is currently a master student in the same university. His research mainly focuses on air combat, machine intelligent and artificial intelligence.E-mail: 15349215326@163.com

Abstract:

Target maneuver trajectory prediction plays an important role in air combat situation awareness and threat assessment. To solve the problem of low prediction accuracy of the traditional prediction method and model, a target maneuver trajectory prediction model based on phase space reconstruction-radial basis function (PSR-RBF) neural network is established by combining the characteristics of trajectory with time continuity. In order to further improve the prediction performance of the model, the rival penalized competitive learning (RPCL) algorithm is introduced to determine the structure of RBF, the Levenberg-Marquardt (LM) and the hybrid algorithm of the improved particle swarm optimization (IPSO) algorithm and the k-means are introduced to optimize the parameter of RBF, and a PSR-RBF neural network is constructed. An independent method of 3D coordinates of the target maneuver trajectory is proposed, and the target manuver trajectory sample data is constructed by using the training data selected in the air combat maneuver instrument (ACMI), and the maneuver trajectory prediction model based on the PSR-RBF neural network is established. In order to verify the precision and real-time performance of the trajectory prediction model, the simulation experiment of target maneuver trajectory is performed. The results show that the prediction performance of the independent method is better, and the accuracy of the PSR-RBF prediction model proposed is better. The prediction confirms the effectiveness and applicability of the proposed method and model.

Key words: trajectory prediction, k-means, improved particle swarm optimization (IPSO), Levenberg-Marquardt (LM), radial basis function (RBF) neural network