An Efficient Implementation of the Generalized Labeled Multi-Bernoulli Filter
Computation
2017-03-01 v2 Applications
Abstract
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 an earlier implementation that involves separate truncations in the prediction and update steps, the proposed implementation requires only one truncation procedure for each iteration. Furthermore, we propose an efficient algorithm for truncating the GLMB filtering density based on Gibbs sampling. The resulting implementation has a linear complexity in the number of measurements and quadratic in the number of hypothesized objects.
Cite
@article{arxiv.1606.08350,
title = {An Efficient Implementation of the Generalized Labeled Multi-Bernoulli Filter},
author = {Ba Ngu Vo and Ba Tuong Vo and Hung Gia Hoang},
journal= {arXiv preprint arXiv:1606.08350},
year = {2017}
}