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

• Published in: IEEE signal processing letters Vol. 23; no. 6; pp. 863 - 867
• Main Authors: Fantacci, Claudio, Papi, Francesco
• Format: Journal Article
• Language: English
• Published: IEEE 01.06.2016
• Subjects: Algorithms; Approximation; Approximation algorithms; Complexity theory; Computer simulation; Density; Finite set statistics (FISST); generalized labeled multi-Bernoulli (GLMB); History; Indexes; marginalized δ-GLMB (Mδ-GLMB); Mathematical analysis; Mathematical model; multisensor; Radar; Radar tracking; random finite set (RFS); Sensors; Tracking; Finite set statistics (FISST); multisensor; generalized labeled multi-Bernoulli (GLMB); marginalized δ-GLMB (Mδ-GLMB); random finite set (RFS)
• ISSN: 1070-9908; 1558-2361
• DOI: 10.1109/LSP.2016.2557078

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.