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Acta Armamentarii ›› 2024, Vol. 45 ›› Issue (12): 4435-4450.doi: 10.12382/bgxb.2023.1078

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Autonomous Attack Decision-making of Missile Swarm on Ground Targets Based on Visual Damage Assessment without Communication

XU Yibo1, YAN Jiarun1, ZENG Zhiwen1,*(), LÜ Yunxiao1, FENG Shiru2, LU Huimin1   

  1. 1 College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, Hunan, China
    2 Shenzhen TFC Technology Co., Ltd., Shenzhen 518000, Guangzhou, China
  • Received:2023-11-03 Online:2024-02-27
  • Contact: ZENG Zhiwen

Abstract:

For the mission scenario of using a cluster of patrol missiles to fight against the ground high-defense mobile targets, an attack decision-making process method based on the height division of patrol missiles with different operational functions is proposed. By deploying the YOLO X and VGG-16 integrated online target recognition and damage assessment network (YOLO-VGGNet) on any patrol missile, the patrol missile can autonomously strike a target based on visual sensing information only under no communication conditions. At the same time, a method for terminal reassessment and target attack point selection is proposed so that the patrol missile can not only reassess the ground target at the terminal of attack when flying to the target, but also locate the feature parts that have not been struck. The nodes of missile swarm can autonomously decide whether to continuely attack and prioritize the attack on the feature parts of target. Simulated results show that under no communication conditions, the proposed YOLO-VGGNet online damage assessment network can not only destroy the targets in the mission area completely, but also reduce the unnecessary ammunition loss and prioritize the attack on the functional parts of the target, which is expected to improve the overall combat effectiveness of missile swarm.

Key words: massively distributed missile swarm operation, reconnaissance-strike-assessment, no communication condition, terminal attack decision-making, visual damage assessment

CLC Number: