Journal
IEEE TRANSACTIONS ON SIGNAL PROCESSING
Volume 65, Issue 8, Pages 1975-1987Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2016.2641392
Keywords
Random finite sets; generalized labeled multi-Bernoulli; multiobject tracking; data association; optimal assignment; ranked assignment; Gibbs sampling
Categories
Funding
- Australian Research Council [DP130104404]
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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.
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