A unified approach to state estimation problems under data and model uncertainties

Daniel Sigalov, Tomer Michaeli, Yaakov Oshman

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

Abstract

We present a unified approach to the problem of state estimation under measurement and model uncertainties. The approach allows formulation of problems such as maneuvering target tracking, target tracking in clutter, and multiple target tracking using a single state-space system with random matrix coefficients. Consequently, all may be solved efficiently using a single IMM algorithm or using a linear optimal filter, derived elsewhere, thus replacing the need for deriving a unique algorithm for each problem.

Original languageEnglish
Title of host publication15th International Conference on Information Fusion, FUSION 2012
Pages2569-2576
Number of pages8
StatePublished - 2012
Event15th International Conference on Information Fusion, FUSION 2012 - Singapore, Singapore
Duration: 7 Sep 201212 Sep 2012

Publication series

Name15th International Conference on Information Fusion, FUSION 2012

Conference

Conference15th International Conference on Information Fusion, FUSION 2012
Country/TerritorySingapore
CitySingapore
Period7/09/1212/09/12

Keywords

  • Maneuvering target tracking
  • clutter and data association
  • hybrid systems
  • multiple target tracking

All Science Journal Classification (ASJC) codes

  • Information Systems

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