Collaborative Localization Algorithm for Joint Node Selection and Power Allocation Based on Cooperative Games

This study presents a novel collaborative positioning framework based on Coalitional Game theory to address reference node selection and resource allocation.By optimizing the coalition structure between nodes, it balances collaboration and competition, enhancing positioning accuracy and resource efficiency.A collaborative positioning algorithm based on economic models is developed, employing an auction mechanism and an improved Shapley value method for power allocation, thereby improving cooperation among agent nodes within a limited energy budget.Additionally, the reference node bidding and coalition formation are modeled as a Markov decision process, and the coalitional-MADDPG algorithm based on multi-agent deep reinforcement learning is used to optimize pricing and purchasing strategies, significantly improving positioning accuracy.Simulation results show the proposed algorithm has significant advantages in terms of positioning accuracy and energy consumption.