An implementation of the multi-sensor generalized labeled multi-Bernoulli filter via Gibbs sampling
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Vo, Ba-Ngu
Vo, Ba Tuong
Date
2017Type
Conference Paper
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Vo, B. and Vo, B.T. 2017. An implementation of the multi-sensor generalized labeled multi-Bernoulli filter via Gibbs sampling.
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20th International Conference on Information Fusion, Fusion 2017 - Proceedings
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School of Electrical Engineering, Computing and Mathematical Science (EECMS)
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Abstract
© 2017 International Society of Information Fusion (ISIF). 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.
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