Generalized multi-Bernoulli filters for track-before-detect of objects from image observations
Pages 89 - 93
Abstract
In this paper, we propose a Generalized Multi-Bernoulli Filter for Track-before-detect (GMB-TBD) of objects from image observations when the objects' influence regions are overlapping. We analyze the overlapping objects' measurement likelihood function, estimate this likelihood function by predicted objects' states and eliminate the objects' overlapping influence on objects' states updating using this estimation. In this filter, the predicted and updated objects' states are strictly assumed as Multi-Bernoulli RFS(Random Finite Set), so it's a truly Multi-Bernoulli based TBD filter and it can be used under either the objects' influence region overlapping or non-overlapping situations. We give the filter's realization steps, prune and extract the objects' tracks by labeling the Multi-Bernoulli components. Lastly, we test the GMB-TBD filter's performance by computer Monte-Carlo simulations.
References
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Index Terms
- Generalized multi-Bernoulli filters for track-before-detect of objects from image observations
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Published In
November 2017
237 pages
ISBN:9781450353847
DOI:10.1145/3163080
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Published: 27 November 2017
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ICSPS 2017
ICSPS 2017: The 9th International Conference on Signal Processing Systems
November 27 - 30, 2017
Auckland, New Zealand
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Overall Acceptance Rate 46 of 83 submissions, 55%
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