An implementation of the multi-sensor generalized labeled multi-Bernoulli filter via Gibbs sampling
© 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...
| Main Authors: | , |
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| Format: | Conference Paper |
| Published: |
2017
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| Online Access: | http://purl.org/au-research/grants/arc/DP170104854 http://hdl.handle.net/20.500.11937/63168 |
| Summary: | © 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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