Improved IMM algorithm for nonlinear maneuvering target tracking

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

Research output: Contribution to journalConference articleOther

19 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
Pages (from-to)4117-4123
Number of pages7
JournalProcedia Engineering
Volume29
DOIs
Publication statusPublished - 22 Mar 2012
Event2012 International Workshop on Information and Electronics Engineering, IWIEE 2012 - Harbin, China
Duration: 10 Mar 201211 Mar 2012

Keywords

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

Cite this

Gao, Liang ; Xing, Jianping ; Ma, Zhenliang ; Sha, Junchen ; Meng, Xiangzhan. / Improved IMM algorithm for nonlinear maneuvering target tracking. In: Procedia Engineering. 2012 ; Vol. 29. pp. 4117-4123.
@article{35b62b93a8ef45a5997ff79d4efd20c7,
title = "Improved IMM algorithm for nonlinear maneuvering target tracking",
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.",
keywords = "Extended Kalman Filter, Interacting multiple model, Nonlinear maneuvering target tracking, Unscented Kalman Filter",
author = "Liang Gao and Jianping Xing and Zhenliang Ma and Junchen Sha and Xiangzhan Meng",
year = "2012",
month = "3",
day = "22",
doi = "10.1016/j.proeng.2012.01.630",
language = "English",
volume = "29",
pages = "4117--4123",
journal = "Procedia Engineering",
issn = "1877-7058",
publisher = "Elsevier",

}

Improved IMM algorithm for nonlinear maneuvering target tracking. / Gao, Liang; Xing, Jianping; Ma, Zhenliang; Sha, Junchen; Meng, Xiangzhan.

In: Procedia Engineering, Vol. 29, 22.03.2012, p. 4117-4123.

Research output: Contribution to journalConference articleOther

TY - JOUR

T1 - Improved IMM algorithm for nonlinear maneuvering target tracking

AU - Gao, Liang

AU - Xing, Jianping

AU - Ma, Zhenliang

AU - Sha, Junchen

AU - Meng, Xiangzhan

PY - 2012/3/22

Y1 - 2012/3/22

N2 - 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.

AB - 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.

KW - Extended Kalman Filter

KW - Interacting multiple model

KW - Nonlinear maneuvering target tracking

KW - Unscented Kalman Filter

UR - http://www.scopus.com/inward/record.url?scp=84858436990&partnerID=8YFLogxK

U2 - 10.1016/j.proeng.2012.01.630

DO - 10.1016/j.proeng.2012.01.630

M3 - Conference article

VL - 29

SP - 4117

EP - 4123

JO - Procedia Engineering

JF - Procedia Engineering

SN - 1877-7058

ER -