Power Allocation for Cooperative Localization in Decentralized Distributed Radar Networks: A Nash Bargaining Game Approach

It is critical for radar networks to balance the target positioning accuracy and low intercept performance by managing transmitting power. However, under the decentralized and distributed condition, each radar node is in a state of incomplete information perception, which brings intractable issues to resource management. To address this challenge, we propose a transmitted power allocation approach based on the Nash bargaining game. We establish a model correlating each radar node's transmitting power to the Cramér–Rao lower bound of target localization. The power allocation problem is then reframed as a Nash bargaining game, with each radar node viewed as a participant. We construct an objective function for each node, which balances achieving desired target positioning accuracy against minimizing interception risks. The existence and uniqueness of the Nash equilibrium in this game model are theoretically proven. A distributed iterative algorithm is introduced to derive the Nash equilibrium solution efficiently. Simulation results demonstrate that our method outperforms the traditional noncooperative and random allocation approaches by significantly lowering interception probability while preserving target localization accuracy. Furthermore, it exhibits superior real-time capabilities compared with the centralized method.