Multi-Sensor Multi-Object Tracking with the Generalized Labeled Multi-Bernoulli Filter
This paper proposes an efficient implementation of the multi-sensor generalized labeled multi-Bernoulli (GLMB) filter. Like its single-sensor counterpart, such implementation requires truncating the GLMB sum. However the single-sensor case requires solving 2-D ranked assignment problems whereas the...
| Main Authors: | , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
2019
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| Subjects: | |
| Online Access: | http://purl.org/au-research/grants/arc/DP170104854 http://hdl.handle.net/20.500.11937/90802 |
| Summary: | This paper proposes an efficient implementation of the multi-sensor generalized labeled multi-Bernoulli (GLMB) filter. Like its single-sensor counterpart, such implementation requires truncating the GLMB sum. However the single-sensor case requires solving 2-D ranked assignment problems whereas the multi-sensor case require solving multi-dimensional ranked assignment problems, which are NP-hard. The proposed implementation 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 quadratic complexity in the number of hypothesized objects and linear in the total number of measurements from all sensors. |
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