Effectiveness Evaluation of Land-based Intelligent Unmanned Combat Systems Based on Graph Convolutional Networks

doi:10.12382/bgxb.2024.0516

Abstract

Abstract:

To address the issues of systemic inadequacies, lack of correlation, and insufficient consideration of complexity in the effectiveness evaluation of land-based intelligent unmanned combat systems, a graph convolutional network (GCN)-based effectiveness evaluationframework is proposed. The framework aims to leverage GCN technology to precisely evaluate the performance of intelligent unmanned combat systems. A comprehensive set of evaluation index systemis established according to the characteristics of land-based intelligent combat, and this system is mapped onto a graph network structure, enabling a highly abstract representation of the unmanned combat system in complex operational environments. The big data analytics and expert knowledge areused to preprocess and engineer the initial dataset for optimizing the quality of input data. The hierarchical structure of the evaluation index system and the interrelationships among its components are deeply explored by applying GCN’s semi-supervised learning mode, thereby achieving a comprehensive evaluation of the effectiveness of land-based intelligent unmanned combat systems. This evaluation framework addresses numerous issues existingin the current evaluation of these systems, offering a dynamic, systematic, and comprehensive solution that demonstrates the application potential of GCN in the field of military technology.

Key words: unmanned combat, land-based intelligent combat, effectiveness evaluation, graph convolutional network, big data

CLC Number:

E917

WAN Zhangbo, HU Jiangang, LI Junjie, CHEN Li, MAO Yukun, YE Mengya. Effectiveness Evaluation of Land-based Intelligent Unmanned Combat Systems Based on Graph Convolutional Networks[J]. Acta Armamentarii, 2024, 45(S1): 271-277.

Figures/Tables 6

Fig.1 Technical framework for evaluating the effectiveness of jntelligent unmanned combat system based on big data

Fig.2 Intelligent unmanned combat effectiveness index system

Fig.3 Methodological framework for evaluating the effectiveness of jntelligent unmanned combat systems based on semi-supervised learning of GCNs

Fig.4 Mapping of index systems

Fig.5 Data preprocessing

Fig.6 Graph convolution and aggregation

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