In this paper, a stochastic networking algorithm based on global state extended kalman-based particle filter on labeled multiBernoulli (GS-EPF-LMB) is proposed for distributed cooperative navigation of multiple robots in intermittent observation or no absolute observation environments. The algorithm models the states and observations using random finite sets and generates labeled multi-Bernoulli particles through three state update strategies: time update, observation update, and display communication. To improve the consistency and localization accuracy of the algorithm, this paper couples relative and absolute observations based on labeled multi - Bernoulli particles, using particle filters to optimize the labeld particle states and constrain state estimation with historical information. In addition, it employs probabilistic data correlation for navigation system state estimation and uses a hierarchical Gaussian model combined with variational Bayesian methods to achieve globally optimal state estimation. The experimental results show that the proposed algorithm achieves a localization accuracy of 0. 11 m. The convergence of localization state covariance is improved by 48. 6% and the accuracy is increased by 11% compared with the GS-CI algorithm.