Distributed Cross-Entropy δ-GLMB Filter for Multi-Sensor Multi-Target Tracking

Augustin Alexandru Saucan, Pramod K. Varshney

Research output: Chapter in Book/Entry/PoemConference contribution

12 Scopus citations


The multi-dimensional assignment problem, and by extension the problem of finding the T-best (i.e., the T most likely) multi-sensor assignments, represent the main challenges of centralized and especially distributed multi-sensor tracking. In this paper, we propose a distributed multi-target tracking filter based on the δ-Generalized Labeled Multi-Bernoulli (6-GLMB) family of labeled random finite set densities. Consensus is reached for high-scoring multi-sensor assignments jointly across the network by employing the cross-entropy method in conjunction with average consensus. This ensures that multi-sensor information is jointly used to select high-scoring multi-assignments without exchanging the measurements across the network and without exploring all possible single-target multi-assignments. In contrast, tracking algorithms that rely on posterior fusion, i.e., merging local posteriors of neighboring nodes until convergence, are suboptimal due to the use of only local information to select the T-best local assignments in the construction of local posteriors. Numerical simulations showcase this performance improvement of the proposed method with respect to a posterior-fusion δ- GLMB filter.

Original languageEnglish (US)
Title of host publication2018 21st International Conference on Information Fusion, FUSION 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Print)9780996452762
StatePublished - Sep 5 2018
Event21st International Conference on Information Fusion, FUSION 2018 - Cambridge, United Kingdom
Duration: Jul 10 2018Jul 13 2018


Other21st International Conference on Information Fusion, FUSION 2018
Country/TerritoryUnited Kingdom


  • average consensus
  • cross entropy
  • distributed tracking
  • GLMB filter
  • random finite sets

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Statistics, Probability and Uncertainty
  • Instrumentation


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