The Labeled Multi-Bernoulli Filter

By Stephan Reuter, B. Vo, Ba-Ngu Vo, Klaus C. J. Dietmayer. IEEE Transactions on Signal Processing, 2014

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Overview of Environment Perception for Intelligent Vehicles (2017, background): “In contrast to explicit data association, implicit data association tracking approaches output a set of object hypotheses in an implicit way, such as particle filter approaches [132], probability hypothesis density (PHD) filtering [133], multi-target multiBernoulli (MeMBer) filtering [134], [135], a”
An Efficient Implementation of the Generalized Labeled Multi-Bernoulli Filter (2016, methodology): “This implementation is also applicable to approximations such as the labeled multiBernoulli (LMB) filter since this filter requires a special case of the δ-GLMB prediction and a full δ-GLMB update to be performed [29].”
Extended Object Tracking: Introduction, Overview and Applications (2016, methodology): “A computationally efficient approximation is the Labeled Multi-Bernoulli (LMB) filter [154], which approximates the GLMB mixture with a single labeled multi-Bernoulli density.”