Labeled Random Finite Sets and Multi-Object Conjugate Priors

By B. Vo. IEEE Transactions on Signal Processing, 2013

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Marginal multi-bernoulli filters: RFS derivation of MHT, JIPDA, and association-based member (2012, background): “This can be made explicit by incorporating a label element into the underlying state space, as recently proposed in [27]; it can easily be shown that each track maintained by TOMB/P would retain a unique label.”
An Efficient Implementation of the Generalized Labeled Multi-Bernoulli Filter (2016, methodology): “A salient feature of the GLMB family is its closure under the multi-object Bayes recursion for the standard multi-object transition kernel and likelihood function [27].”
The Labeled Multi-Bernoulli Filter (2014, methodology): “Generalized Labeled Multi-Bernoulli RFS A generalized labeled multi-Bernoulli (GLMB) RFS [35] is a labeled RFS with state space X and (discrete) label space L distributed according to”
A Tutorial on Bernoulli Filters: Theory, Implementation and Applications (2013, neutral): “The latest development is the exact closed form recursion or conjugate result known as the Generalized Labeled Multi-Bernoulli filter in [14], [93].”
Extended Object Tracking: Introduction, Overview and Applications (2016, methodology): “1) PHD and CPHD ﬁlters: The ﬁrst order moment of the multi-object state is called the PHD 10 , and can be said to be to a random set as the expected value is to a random variable.”