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.

在本文中，提出了一种有效的多模型广义标记多伯努利滤波器（MM-GLMB）的实现方案，用于跟踪多个机动目标。为了减轻冗余组件的产生，将MM-GLMB滤波器的原始两阶段实现集成到一个步骤中，带来的好处是每次迭代仅需要一个截断过程。在这项研究中，作者充分考虑了吉布斯采样的收敛行为，以提高收敛速度。针对MM-GLMB滤波器的实现，提出了一种基于格斯高斯分布的阻塞Gibbs采样解决方案，以极大地减轻计算量。数值仿真证明了该算法的有效性，计算成本较低。