基于REINFORCE算法的组网测向选站方法OA

To address the issue of the poor timeliness and difficulty of real-time tracking in traditional networked direction-finding methods when handling fast-moving targets with short signal burst dura-tions,a station selection method based on the REINFORCE algorithm is proposed.A Markov decision process(MDP)framework is employed to decompose the station combination process into a multi-step sequential decision process.By selecting stations one by one,the combinatorial optimization problem is transformed into a sequential decision problem.The positional relationship between stations and the target is used as the state space,the number of stations in the combination constrains the agent's learn-ing steps,action masking is applied to the selected stations,and the geometrical dilution of precision(GDOP)serves as the reward function to evaluate the quality of station selection schemes.Experimen-tal results demonstrate that the proposed method significantly outperforms both the exhaustive method and the genetic algorithm in terms of time complexity,while achieving comparable localization accu-racy to the exhaustive method.Furthermore,it exhibits strong generalization ability by learning the mapping between station spatial configuration and GDOP,enabling effective network deployment deci-sions for unseen target scenarios.