Integrated data association and bias estimation in the presence of missed detections

Hongyan Zhu, Chen Wang, Wen Jiang, Chongzhao Han, Yan Lin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

This paper is concerned with performing the measurement-to-measurement association and bias estimation jointly in the presence of missed detections. An integrated mix integer programming (MINLP) model is established to determine the correspondence between local measurements and estimate sensor biases simultaneously. An alternation optimization mechanism is employed to solve the complicated MINLP model. A recursive version for bias estimation is developed that provides an access to deal with the measurement data sequentially. Monte Carlo simulation results are presented to illustrate our findings, as also demonstrating the effectiveness of the proposed approach.

Original languageEnglish
Title of host publicationFUSION 2014 - 17th International Conference on Information Fusion
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9788490123553
StatePublished - 3 Oct 2014
Event17th International Conference on Information Fusion, FUSION 2014 - Salamanca, Spain
Duration: 7 Jul 201410 Jul 2014

Publication series

NameFUSION 2014 - 17th International Conference on Information Fusion

Conference

Conference17th International Conference on Information Fusion, FUSION 2014
Country/TerritorySpain
CitySalamanca
Period7/07/1410/07/14

Keywords

  • bias estimation
  • data association
  • mixed integer nonlinear programming (MINLP)
  • sensor biases

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