Improved IMM algorithm for nonlinear maneuvering target tracking

Liang Gao, Jianping Xing, Zhenliang Ma, Junchen Sha, Xiangzhan Meng

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

21 Citations (Scopus)

Abstract

Devoted to the problem of state estimation of discrete-time stochastic systems, SIMM (Scalar-Weight Interacting Multiple Model) and MIMM (Matrix-Weight Interacting Multiple Model) methods are proposed by X. Fu, in which the filter outputs are combined based on two optimal multi-model fusion criterions weighted by scalars and general matrices, respectively. In this paper, four improved IMM algorithms (EKF-SIMM, EKF-MIMM, UKF-SIMM and UKF-MIMM) are presented for nonlinear maneuvering target tracking based on SIMM and MIMM. The proposed improved algorithms can receive the optimal state estimations of target in the nonlinear minimum variance sense. Experiments results verify the effectiveness of the proposed algorithms by comparing with EKF-IMM and UKFIMM. And the proposed algorithms have an absolute advantage in the velocity estimation. In particular, UKF-MIMM is obviously better than EKF-IMM and UKF-IMM in accuracy while EKF-SIMM is superior in elapsed time. Therefore, the proposed algorithms can be competitive alternatives to the classical IMM-based filter algorithms for nonlinear maneuvering target tracking.

Original languageEnglish
Title of host publication2012 International Workshop on Information and Electronics Engineering, IWIEE 2012
PublisherElsevier
Pages4117-4123
Number of pages7
DOIs
Publication statusPublished - 22 Mar 2012
Event2012 International Workshop on Information and Electronics Engineering, IWIEE 2012 - Harbin, China
Duration: 10 Mar 201211 Mar 2012

Publication series

NameProcedia Engineering
PublisherElsevier
Volume29
ISSN (Print)1877-7058

Conference

Conference2012 International Workshop on Information and Electronics Engineering, IWIEE 2012
CountryChina
CityHarbin
Period10/03/1211/03/12

Keywords

  • Extended Kalman Filter
  • Interacting multiple model
  • Nonlinear maneuvering target tracking
  • Unscented Kalman Filter

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