Swarm intelligence based clustering and localizing methods for loitering munitions in a satellite denied environment

In the networking of loitering munitions during a battle, clustering and localizing algorithms become a major problem because of their highly dynamic topological structure, incomplete connectivity, and limited energy. This paper proposed swarm intelligence based collaborative localizing, clustering, and routing scheme for an ad hoc network of loitering munitions in a satellite denied environment. A hybrid algorithm was first devised by integrating an improved coyote optimization algorithm with a simplified grey wolf optimizer under the sinusoidal crossover strategy. The performance of this algorithm was considerably improved thanks to integration. On this basis, a swarm intelligence based localizing algorithm was presented. Bounding cubes were created to reduce the initial search space, which effectively lowered the localizing error. Second, an energy-saving clustering algorithm based on the hybrid algorithm was put forward to enhance the clustering efficiency by virtue of grey wolf hierarchy. Meanwhile, an analysis model was developed to determine the optimal number of clusters using the lowest possible number of transmissions. Ultimately, a compressed sensing routing scheme based on the hybrid algorithm was proposed to transmit data from a cluster head to a base station. This algorithm constructed an efficient routing tree from the cluster head to the base station, so as to reduce the routing delay and transmission count. As revealed in the results of simulation experiments, the proposed collaborative localizing, clustering and routing algorithms achieved better performance than other popular algorithms employed in various scenarios.