Quick identification of guidance law for an incoming missile using multiple-model mechanism

Yinhan WANG, Shipeng FAN*, Jiang WANG, Guang WU

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

Abstract

A guidance law parameter identification model based on Gated Recurrent Unit (GRU) neural network is established. The scenario of the model is that an incoming missile (called missile) attacks a target aircraft (called aircraft) using Proportional Navigation (PN) guidance law. The parameter identification is viewed as a regression problem in this paper rather than a classification problem, which means the assumption that the parameter is in a finite set of possible results is discarded. To increase the training speed of the neural network and obtain the nonlinear mapping relationship between kinematic information and the guidance law parameter of the incoming missile, an output processing method called Multiple-Model Mechanism (MMM) is proposed. Compared with a conventional GRU neural network, the model established in this paper can deal with data of any length through an encoding layer in front of the input layer. The effectiveness of the proposed Multiple-Model Mechanism and the performance of the guidance law parameter identification model are demonstrated using numerical simulation.

Original languageEnglish
Pages (from-to)282-292
Number of pages11
JournalChinese Journal of Aeronautics
Volume35
Issue number9
DOIs
Publication statusPublished - Sept 2022

Keywords

  • Gated recurrent unit
  • Multiple-model mechanism
  • Neural networks
  • Parameter identification
  • Regression models

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