  This paper proposes an efficient implementation of the generalized labeled
multi-Bernoulli (GLMB) filter by combining the prediction and update into a
single step. In contrast to the original approach which involves separate
truncations in the prediction and update steps, the proposed implementation
requires only one single truncation for each iteration, which can be performed
using a standard ranked optimal assignment algorithm. Furthermore, we propose a
new truncation technique based on Markov Chain Monte Carlo methods such as
Gibbs sampling, which drastically reduces the complexity of the filter. The
superior performance of the proposed approach is demonstrated through extensive
numerical studies.
