Joint estimation of state and sensor systematic error in hybrid system

Lin Zhou, Quan Pan, Yan Liang, Zhenlu Jin

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

2 Scopus citations

Abstract

Consider the hybrid systems with nonlinear property, and the sensor measurements with unknown and time-varying systematic error in this paper. In order to obtain the joint least square (LS) estimation of state and systematic error, a new method - JE-EM (joint estimation-expectation maximization) is proposed. In this paper, the relationship between the sensor systematic error estimation and state estimation is derived, which can be described by the framework of EM. Due to the character of the hybrid system, the target state is estimated by the IMM with PF filter. Based on the above relationship, systematic error is iteratively estimated by the framework of EM. Simulation results with a maneuvering target tracking scenario show the effectiveness of the proposed method.

Original languageEnglish
Title of host publication15th International Conference on Information Fusion, FUSION 2012
Pages969-975
Number of pages7
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

  • Expectation maximization (EM)
  • Hybrid system
  • Interacting multiple model (IMM)
  • Particle filter (PF)
  • Systematic error

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