Scalable Multisensor Multitarget Tracking Using the Marginalized $\delta$-GLMB Density

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 δ-generalized labeled multi-Bernoulli (Mδ-GLMB) densities, which can be used to define a principled approximation to the δGLMB density representing the true posterior in the sense of the multitarget Bayes filter. We derive the update equations of an MδGLMB density that matches the δ-GLMB density in cardinality distribution and first moment, as well as minimizes the Kullback- Leibler divergence w.r.t. the true δ-GLMB density over the class of Mδ-GLMB densities. The proposed Mδ-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.