Most existing distributed state estimation approaches assume that the position of agent in multi-agent system (MAS) is precisely known. However, in target tracking applications, the position of agent is noisy, which makes traditional distributed state estimation methods suffer from accuracy reduction. This paper focuses on developing a novel hybrid consensus-based variational Bayesian filter (HCVB) for target tracking with position uncertainty in nonlinear MAS. To improve the nonlinear approximation accuracy, high-degree cubature information filter framework is used. To cope with agent position uncertainty, the measurement update is derived by using variational Bayesian (VB) method to approximate the joint posterior distribution of the target state and agent position, which can estimate the state and agent position jointly. To decrease the required consensus iterations, a hybrid consensus method is proposed, which can achieve global consistency of all agents with a few consensus iterations. Simulations on UAV tracking in MAS verify the superiority of the proposed method.
A novel hybrid consensus-based variational Bayesian filter with position uncertainty in multi-agent system
2023-08-01
10 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
HYBRID SAMPLING BAYESIAN OCCUPANCY FILTER
British Library Conference Proceedings | 2014
|Consensus in multi-agent system with communication constraints based on asynchronous dynamics
BASE | 2018
|An Improved Variational Bayesian Information Filter with Measurement Loss for Underwater Navigation
Springer Verlag | 2021
|