The Labeled Multi-Bernoulli Filter
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This paper proposes a generalization of the multi- Bernoulli filter called the labeled multi-Bernoulli filter that outputs target tracks. Moreover, the labeled multi-Bernoulli filter does not exhibit a cardinality bias due to a more accurate update approximation compared to the multi-Bernoulli filter by exploiting the conjugate prior form for labeled Random Finite Sets. The proposed filter can be interpreted as an efficient approximation of the $delta$-Generalized Labeled Multi-Bernoulli filter. It inherits the advantages of the multi-Bernoulli filter in regards to particle implementation and state estimation. It also inherits advantages of the $delta$ -Generalized Labeled Multi-Bernoulli filter in that it outputs (labeled) target tracks and achieves better performance.
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Reuter, S.; Vo, Ba Tuong; Vo, Ba-Ngu; Dietmayer, K. (2014)In this paper, we propose the labeled multi-Bernoulli filter which explicitly estimates target tracks and provides a more accurate approximation of the multi-object Bayes update than the multi-Bernoulli filter. In particular, ...
Jones, B.; Vo, Ba Tuong; Vo, Ba-Ngu (2016)Space-object tracking systems require robust and accurate methods of multi-target state estimation and prediction. This paper presents the application of labeled multi-Bernoulli filters for space-object tracking, and ...
Beard, M.; Reuter, S.; Granström, K.; Vo, Ba-Ngu; Vo, Ba Tuong; Scheel, A. (2016)Targets that generate multiple measurements at a given instant in time are commonly known as extended targets. These present a challenge for many tracking algorithms, as they violate one of the key assumptions of the ...