In this paper,a stochastic networking algorithm based on global state extended kalman-based particle filter on labeled multi-Bernoulli(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 0.1 1 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.
维普
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