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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 &#x03B4;-generalized labeled multi-Bernoulli (M&#x03B4;-GLMB) densities, which can be used to define a principled approximation to the &#x03B4;GLMB density representing the true posterior in the sense of the multitarget Bayes filter. We derive the update equations of an M&#x03B4;GLMB density that matches the &#x03B4;-GLMB density in cardinality distribution and first moment, as well as minimizes the Kullback- Leibler divergence w.r.t. the true &#x03B4;-GLMB density over the class of M&#x03B4;-GLMB densities. The proposed M&#x03B4;-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.

ieee signal processing letters

Scalable Multisensor Multitarget Tracking Using the Marginalized <inline-formula><tex-math notation="LaTeX">$\delta$</tex-math></inline-formula>-GLMB Density

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