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Expert Knowledge-Assisted Drone Combat Strategy via Intention Interpretation

  • Xindi Wang
  • , Hao Liu*
  • , Dawei Liu
  • , Ming Cheng
  • , Chenguang Liu
  • , Xiaoguang Wang
  • , Mutian Guo
  • *Corresponding author for this work
  • Beihang University
  • China Research and Development Academy of Machinery Equipment
  • Norinco Group Air Ammunition Research Institute

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, a drone game strategy generation model is designed in a denied environment. A typical nonlinear model of drone formation dynamics is first constructed. A behavior prediction algorithm based on neural network is then utilized to predict the trajectory and behavior characteristics of the attack drones, and a neural network algorithm achieves intelligent interpretation of the attack drones intention based on the result of the prediction. A game strategy generation algorithm assisted by expert knowledge and game experience library is finally proposed using the result of the intention interpretation. These algorithms overcome the difficulty of obtaining the game strategy under the deceptive and elusive characteristics of attacking drones and realize high real-time and pertinence of decision-making. Simulation results of typical scenarios are provided to verify the effectiveness of the proposed algorithm model.

Original languageEnglish
Title of host publication2023 42nd Chinese Control Conference, CCC 2023
PublisherIEEE Computer Society
Pages8143-8147
Number of pages5
ISBN (Electronic)9789887581543
DOIs
StatePublished - 2023
Event42nd Chinese Control Conference, CCC 2023 - Tianjin, China
Duration: 24 Jul 202326 Jul 2023

Publication series

NameChinese Control Conference, CCC
Volume2023-July
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference42nd Chinese Control Conference, CCC 2023
Country/TerritoryChina
CityTianjin
Period24/07/2326/07/23

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