Evaluate and Make Decisions on Cooperative Positioning Systems in Weighted Pythagorean Hesitant Fuzzy Environment Combined With GAN

Current cooperative positioning schemes are complex, and their performances vary, urgently necessitating evaluation. In view of the specific characteristics of cooperative positioning systems (CPSs) in terms of physical data, decision-making factors, and the professionalism of decisions, this study aims to construct a reasonable multicriteria cooperative positioning decision-making (MCCPDM) evaluation framework. The study applies generative adversarial networks (GANs) to the decision-making process, proposes a weighted fusion of Pythagorean fuzzy sets (PFS) and hesitant fuzzy sets (HFS), and provides a method for determining the weights of decision-making factors that combines objective physical entropy information with subjective multilevel judgments. An MCCPDE method integrating technique for order preference by similarity to ideal solution (TOPSIS) is developed under the weighted Pythagorean hesitant fuzzy environment. Finally, through experiments evaluating cooperative positioning schemes, the proposed framework is proved to be highly effective and practical in cooperative positioning evaluation issues, and the method presented in this article surpasses the best baseline method by two orders of magnitude in terms of optimal ranking proximity.