Resolvable group target tracking based on joint GLMB filter
Aiming at the problem of association errors between group targets close to each other in the joint generalized labeled multi Bernoulli(J-GLMB)filtering algorithm,a resolvable group target tracking algorithm based on HGM-J-GLMB filter is proposed by combining hypergraph matching(HGM).Firstly,the J-GLMB filter is used to estimate the state,number,and trajectory information of each target in the group,and the HGM results are used to improve the correlation performance between measurement states and prediction states.Secondly,the adjacency matrix is calculated by graph theory to obtain group structure information and the number of subgroups.Subsequently,the collaborative noise is estimated by group structure information to correct the predicted state of the target.Finally,the filtering effect is improved through smoothing algorithms,and a trajectory length threshold is set to achieve the goal of eliminating short trajectories while maintaining a smooth state.Simulation experiment results show that the proposed algorithm can effectively improve the performance of group target tracking in linear systems.
multi-target trackingjoint generalized labeled multi-Bernoulli(J-GLMB)filterresolvable group targethypergraph matching(HGM)
万方数据
维普
