Improved variational inference for tracking in clutter

Jason L. Pacheco, Erik B. Sudderth

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

Abstract

We apply the expectation propagation (EP) algorithm to temporally track targets using sensors that produce spurious clutter detections, and may sometimes fail to detect the true target. The variational inference framework underlying EP allows the tracker to be easily adapted to varying measurement models. We develop variants of EP based on single Gaussian and Gaussian mixture approximations of posterior target location distributions, which offer a tradeoff between accuracy and computational complexity. Experiments show improved tracking accuracy and uncertainty estimation relative to widely used baseline tracking algorithms.

Original languageEnglish (US)
Title of host publication2012 IEEE Statistical Signal Processing Workshop, SSP 2012
Pages852-855
Number of pages4
DOIs
StatePublished - 2012
Externally publishedYes
Event2012 IEEE Statistical Signal Processing Workshop, SSP 2012 - Ann Arbor, MI, United States
Duration: Aug 5 2012Aug 8 2012

Publication series

Name2012 IEEE Statistical Signal Processing Workshop, SSP 2012

Conference

Conference2012 IEEE Statistical Signal Processing Workshop, SSP 2012
Country/TerritoryUnited States
CityAnn Arbor, MI
Period8/5/128/8/12

Keywords

  • Bayesian inference
  • expectation propagation
  • target tracking
  • variational methods

ASJC Scopus subject areas

  • Signal Processing

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