A multiple-model generalized labeled multi-Bernoulli filter based on blocked Gibbs sampling for tracking maneuvering targets

•The blocked Gibbs sampling on the lattice Gaussian distribution with a suitable variance is used to effectively solve truncation problem in MM-GLMB filter.•The MM-GLMB filter based on blocked Gibbs sampling is proposed to solve tracking problem of the multiple targets with high maneuverability.•The modified Gram–Schmidt method is adopted to improve the performance of the blocked Gibbs sampling. In this paper, an efficient implementation of the multiple-model generalized labeled multi-Bernoulli filter (MM-GLMB) is presented for tracking multiple maneuvering targets. To alleviate the generation of the redundant components, the original two-staged implementations of MM-GLMB filter are integrated into a single step bringing the benefit that only one truncation procedure is required per iteration. In this study, the authors take the convergence behavior of the Gibbs sampling into full consideration to improve the convergence rate. The blocked Gibbs sampling over lattice Gaussian distribution based solution to the implementation of MM-GLMB filter is proposed to greatly relax the computational load. The numerical simulations demonstrate the efficacy of the proposed algorithm with low computational cost.