Optimization Method Based on K-WPA for Multinode Cooperative Localization Formation Grouping

Multinode cooperative system with flexible grouping capabilities will become a future development trend to adapt well to the complex and dynamic mission requirements. To address the challenge of cooperative node selection in multinode cooperative localization, this study proposes an optimization algorithm for formation grouping in multinode cooperative localization based on the K-means algorithm and the wolf pack algorithm (WPA) (referred to as K-WPA). The algorithm incorporates more practical constraints to guide multinode cluster grouping, thereby improving the efficiency of cluster grouping. In accordance with the clustering results, the population update process of the WPA is optimized to avoid convergence to local optima. By using the Fisher information matrix, the objective function of the WPA is designed, and the optimization process of formation grouping is evaluated. Dynamic grouping simulations are conducted for cooperative systems with 20, 30, and 50 nodes. Results indicate that the proposed K-WPA method improves positioning accuracy by up to 41.24% compared to fixed grouping. Furthermore, the KWPA algorithm combining space division and parallel grouping optimization maintains the average execution time within 1 s for the thousand-node swarm.