Complex Electromagnetic Space Combat System-of-systems Modeling and Key Node Identification Method

TL;DR

This work addresses the challenge of modeling complex electromagnetic space within a Combat System-of-Systems (CSoS) and identifying key nodes that influence overall combat effectiveness. It introduces a networked modeling framework that classifies nodes (Detection, Communication/Decision, Interference, Target) and relationship types, with edge weights representing interaction effectiveness and probability. Capability is computed via a cascade of cycle-level metrics, Cap_{cycle} = $\prod_{e\in E_{cycle}} Cap_{e} \cdot P_{e}$, aggregated to Cap_{CSoS} = $1 - \prod_{cycle\in C} (1 - Cap_{cycle})$, and key nodes are ranked using a node-deletion metric CRT_{X}^{SoS} = { 0, Cap_{SoS-{X}} > Cap_{SoS}, (Cap_{SoS} - Cap_{SoS-{X}})/Cap_{SoS} \times 100\% } . The case study on an aircraft carrier fleet demonstrates higher interpretability and alignment with expert intuition compared to classical centrality measures, validating the method's applicability to planning and defense. This approach offers a scalable, physics-informed way to quantify CSoS performance in electromagnetic space and to target critical nodes for defense or offense.

Abstract

With the application of advanced science and technology in the military field, modern warfare has developed into a confrontation between systems. The combat system-of-systems (CSoS) has numerous nodes, multiple attributes and complex interactions, and its research and analysis are facing great difficulties. Electromagnetic space is an important dimension of modern warfare. Modeling and analyzing the CSoS from this perspective is of great significance to studying modern warfare and can provide a reference for the research of electromagnetic warfare. In this study, the types of nodes and relationships in the complex electromagnetic space of CSoS are first divided, the important attributes of the combat nodes are extracted, and the relationship weights are normalized to establish a networked model. On this basis, the calculation method of CSoS combat effectiveness based on the combat cycle is proposed, and then the identification and sorting of key nodes can be realized by the node deletion method. Finally, by constructing an instance of aircraft carrier fleet confrontation, the feasibility of this method has been verified, and the experimental results have been compared with classical algorithms to demonstrate the advanced nature of this method.

Figures (4)

• Figure 1: Attribute classification of nodes.
• Figure 2: (a) Fitting function images of SNR and BER;(b) Function image of BER and communication efficiency
• Figure 3: Example of an interference relation.
• Figure 4: Schematic diagram of the networked CSoS model with only nodes and directed weighted edges.