Critical Node Identification of Multi-UUV Formation Based on Network Structure Entropy

Yi Chen, Lu Liu, Xiaomeng Zhang, Wei Qiao, Ranzhen Ren, Boyu Zhu, Lichuan Zhang, Guang Pan, Yang Yu

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

In order to identify and attack the multi-UUV (unmanned underwater vehicle) groups, this paper proposes a method for identifying the critical nodes of multi-UUV formations. This method helps in combating multi-UUV formations by identifying the key nodes to attack them. Moreover, these multi-UUV formations are considered to have an unknown structure as the research object. Therefore, the network structure of the formation is reconstructed according to its space–time trajectory, and the importance of nodes is determined based on network structure entropy. As for the methodology, firstly, based on the swarm intelligence behavior method, the motion similarity of multi-UUV nodes in the formation is analyzed in pairs; furthermore, the leader–follower relationship and the network structure of the formation are calculated successively. Then, based on this network structure, the importance of the network nodes is further determined by the network structure entropy method. Finally, through simulation and experiments, it is verified that the algorithm can accurately construct the network structure of the unknown multi-UUV formation, and the accuracy of the calculated time delay data reaches 84.6%, and compared with the traditional information entropy algorithm, the ordering of the important nodes obtained by this algorithm is more in line with the underwater formation network.

Original languageEnglish
Article number1538
JournalJournal of Marine Science and Engineering
Volume11
Issue number8
DOIs
StatePublished - Aug 2023

Keywords

  • critical node
  • formation identification
  • multi-UUV formation
  • network reconstruction
  • network structural entropy

Fingerprint

Dive into the research topics of 'Critical Node Identification of Multi-UUV Formation Based on Network Structure Entropy'. Together they form a unique fingerprint.

Cite this