Name
Abstract | ||
|---|---|---|
Drones and drone swarms, characterized by their low price and ease of deployment, are being utilized to launch assaults, and presenting a great threat to the public and homeland security in recent years. Countering drones or drone swarms is thus of great significance. Some effective counter approaches for a small group of drones have been studied. However, these approaches may be insufficient to counter a large-scale drone swarm due to the lack of efficient time-saving detecting technologies. Towards this end, this paper proposes a fast counter approach to deprive the drone swarm of its coordination and clustering capabilities in a short time by splitting the drone swarm into several unconnected components. To achieve efficient splitting, two efficient algorithms for searching critical nodes are proposed, namely, the genetic algorithm and the particle swarm optimization algorithm. Extensive simulation results are presented to validate the superior performances of the proposed two algorithms for splitting the drone swarms in different formations. The results show that the drone swarms lose their coordination ability as they are forced to split into multiple components with a constraint of group size. The high accuracy and efficiency of the proposed algorithms are also verified by a series of comparative experiments. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TVT.2022.3178821 | IEEE Transactions on Vehicular Technology |
Keywords | DocType | Volume |
Drone swarm,counter,critical node,connected component,genetic algorithm,partical swarm optimization | Journal | 71 |
Issue | ISSN | Citations |
9 | 0018-9545 | 0 |
PageRank | References | Authors |
0.34 | 24 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wu Chen | 1 | 0 | 0.34 |
| Xue Meng | 2 | 0 | 0.34 |
| Jiajia Liu | 3 | 1372 | 94.60 |
| Guo Hongzhi | 4 | 246 | 21.67 |
| Bomin Mao | 5 | 265 | 13.95 |