An implementation of the multi-sensor generalized labeled multi-Bernoulli filter via Gibbs sampling
Ba‐Ngu Vo, Ba-Tuong Vo · 2017
This paper proposes an efficient implementation of the multi-sensor generalized labeled multi-Bernoulli (GLMB) filter. The solution exploits the GLMB joint prediction and update together with a new technique for truncating the GLMB filtering density based on Gibbs sampling. The resulting algorithm has a complexity in the order of the product of the number of measurements from each sensor, and quadratic in the number of hypothesized objects.