Distributed mean-field-type filters for big data assimilation

Jian Gao, Hamidou Tembine

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The combination of observed data and dynamical models of mean-field type over networked systems is a challenging problem because of non-linearity, high dimensionality and partial observations. In many networked systems, the effective extraction and utilization of the information contained in observed data should be accomplished by exploiting the availability of accurate predictive, proactive tools of mean-field type dynamical systems. Incorporating observed big data into dynamical models of mean-field type has two problems. One is the curse of dimensionality, and the other is the control of error accumulation. This paper presents a distributed mean-field filter (DMF) framework for large scale networked systems. The proposed filter exploits the topology of the network and decomposes it into highly independent components with respect to the marginal mean-field correlations. The upper bound of global filtering error can be estimated using mean-field-type game theory. Numerical experiments in two object tracking scenarios are carried out to illustrate the performance of our algorithm. Evaluation results show that DMF significantly outperforms the existing filtering algorithms.

Original languageEnglish (US)
Title of host publicationProceedings - 18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016
EditorsLaurence T. Yang, Jinjun Chen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1446-1453
Number of pages8
ISBN (Electronic)9781509042968
DOIs
StatePublished - Jan 20 2017
Event18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016 - Sydney, Australia
Duration: Dec 12 2016Dec 14 2016

Publication series

NameProceedings - 18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016

Other

Other18th IEEE International Conference on High Performance Computing and Communications, 14th IEEE International Conference on Smart City and 2nd IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2016
CountryAustralia
CitySydney
Period12/12/1612/14/16

Keywords

  • Filtering
  • Mean-field
  • Vehicle tracking

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems

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