Tracking Split Group with δ-Generalized Labeled Multi-Bernoulli Filter

As target splitting is not considered in the initial development of δ-generalized labeled multi-Bernoulli (δ-GLMB) filter, the scenarios where the new targets appearing conditioned on the preexisting one are not readily addressed by this filter. In view of this, we model the group target as gamma Ga...

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Bibliographic Details
Published in:Journal of sensors 2019, Vol.2019 (2019), p.1-12
Main Authors: Gan, Linhai, Wang, Gang
Format: Article
Language:English
Subjects:
Algorithms
Computer simulation
Computing time
Conditioning
Iterative methods
Performance evaluation
Splitting
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Summary:As target splitting is not considered in the initial development of δ-generalized labeled multi-Bernoulli (δ-GLMB) filter, the scenarios where the new targets appearing conditioned on the preexisting one are not readily addressed by this filter. In view of this, we model the group target as gamma Gaussian inverse Wishart (GGIW) distribution and derive a δ-GLMB filter based on the group splitting model, in which the target splitting event is investigated. Two simplifications of the approach are presented to improve the computing efficiency, where with splitting detection, we need not to predict the splitting events of all the GGIW components in every iteration. With component combination applied in adaptive birth, a redundant modeling for a newborn target or preexisting target could be avoided. Moreover, a method for labeling performance evaluation of the algorithm is provided. Simulations demonstrate the effectiveness of the proposed approach.
ISSN:1687-725X
1687-7268
DOI:10.1155/2019/9278725