Network Planning Methodology for Unmanned Perception Networks

Addressing critical challenges in unmanned sensing networks, ambiguous topological structures and inadequate networking methodologies, this study establishes a multilayer architecture based on information-value gain mechanisms. We formalize the networking planning problem as a distance-driven convergence task from sensing to combat nodes, proposing a traction-based hierarchical clustering algorithm as the solution. The algorithm incorporates a dual-optimization mechanism: 1) Mean Clustering Phase: Efficiently aggregates local scattered nodes into preliminary clusters; 2) Hierarchical Clustering Phase: Progressively abstracts network topology into a unified sensing network through iterative refinement. Military-specific innovations include: 1) Outlier Traction Strategy: Dynamically reassigns remote nodes to optimize connectivity; 2) Threat-Zone Avoidance: Embeds battlefield geographical constraints during topology formation The simulation experiments verified the rationality of the networking model proposed in this study and the feasibility of the improvement strategies, which can significantly enhance the networking efficiency and practical adaptability in complex battlefield environments.