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Scalable Multisensor Multitarget Tracking Using the Marginalized δ-GLMB Density
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DOI：10.1109/LSP.2016.2557078.png)
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Abstract
En 中文
Existing multisensor multitarget tracking solutions have complexities that grow super-exponentially w.r.t. the number of sensors. In this letter, we propose a novel algorithm for multisensor multitarget tracking that is scalable w.r.t. the number of sensors. Our approach is based on the class of marginalized delta-generalized labeled multi-Bernoulli (M delta-GLMB) densities, which can be used to define a principled approximation to the delta-GLMB density representing the true posterior in the sense of the multitarget Bayes filter. We derive the update equations of an M delta-GLMB density that matches the delta-GLMB density in cardinality distribution and first moment, as well as minimizes the Kullback-Leibler divergence w.r.t. the true delta-GLMB density over the class of M delta-GLMB densities. The proposed M delta-GLMB density is then used to define an approximate multisensor sequential update step. Simulations in multisensor scenarios with radar and range-only measurements verify the applicability of the proposed approach.
Keywords:
Finite set statistics (FISST)
generalized labeled multi-Bernoulli (GLMB)
marginalized delta-GLMB (M delta-GLMB)
multisensor
random finite set (RFS)
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