State estimation for jump Markov linear systems with uncompensated biases

Wenling Li; Yingmin Jia; Junping Du; Jun Zhang; Deyuan Meng

State estimation for jump Markov linear systems with uncompensated biases

Wenling Li, Yingmin Jia, Junping Du, Jun Zhang, Deyuan Meng

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper studies the problem of state estimation for jump Markov linear systems with uncompensated biases. By describing the state and the measurement biases as additive random variables, a suboptimal filter has been developed by applying the basic interacting multiple model (IMM) approach. To derive a precise representation of the biases contributions to the state estimation, three auxiliary matrices are introduced with respect to the correlation between the state estimation errors and the biases, which helps to derive mode-conditioned estimates in the framework of the IMM. A numerical example involving tracking a maneuvering target is provided to compare the performance of the proposed filter with that of the augmented state filter.

Original language	English
Title of host publication	2013 American Control Conference, ACC 2013
Pages	4903-4908
Number of pages	6
Publication status	Published - 2013
Externally published	Yes
Event	2013 1st American Control Conference, ACC 2013 - Washington, DC, United States Duration: 17 Jun 2013 → 19 Jun 2013

Publication series

Name	Proceedings of the American Control Conference
ISSN (Print)	0743-1619

Conference

Conference	2013 1st American Control Conference, ACC 2013
Country/Territory	United States
City	Washington, DC
Period	17/06/13 → 19/06/13

Keywords

Interacting multiple model
Jump Markov linear system
Uncompensated bias

Cite this

@inproceedings{db2a7a52e35349158cb4e2b81a8f9c47,

title = "State estimation for jump Markov linear systems with uncompensated biases",

abstract = "This paper studies the problem of state estimation for jump Markov linear systems with uncompensated biases. By describing the state and the measurement biases as additive random variables, a suboptimal filter has been developed by applying the basic interacting multiple model (IMM) approach. To derive a precise representation of the biases contributions to the state estimation, three auxiliary matrices are introduced with respect to the correlation between the state estimation errors and the biases, which helps to derive mode-conditioned estimates in the framework of the IMM. A numerical example involving tracking a maneuvering target is provided to compare the performance of the proposed filter with that of the augmented state filter.",

keywords = "Interacting multiple model, Jump Markov linear system, Uncompensated bias",

author = "Wenling Li and Yingmin Jia and Junping Du and Jun Zhang and Deyuan Meng",

year = "2013",

language = "English",

isbn = "9781479901777",

series = "Proceedings of the American Control Conference",

pages = "4903--4908",

booktitle = "2013 American Control Conference, ACC 2013",

note = "2013 1st American Control Conference, ACC 2013 ; Conference date: 17-06-2013 Through 19-06-2013",

}

TY - GEN

T1 - State estimation for jump Markov linear systems with uncompensated biases

AU - Li, Wenling

AU - Jia, Yingmin

AU - Du, Junping

AU - Zhang, Jun

AU - Meng, Deyuan

PY - 2013

Y1 - 2013

N2 - This paper studies the problem of state estimation for jump Markov linear systems with uncompensated biases. By describing the state and the measurement biases as additive random variables, a suboptimal filter has been developed by applying the basic interacting multiple model (IMM) approach. To derive a precise representation of the biases contributions to the state estimation, three auxiliary matrices are introduced with respect to the correlation between the state estimation errors and the biases, which helps to derive mode-conditioned estimates in the framework of the IMM. A numerical example involving tracking a maneuvering target is provided to compare the performance of the proposed filter with that of the augmented state filter.

AB - This paper studies the problem of state estimation for jump Markov linear systems with uncompensated biases. By describing the state and the measurement biases as additive random variables, a suboptimal filter has been developed by applying the basic interacting multiple model (IMM) approach. To derive a precise representation of the biases contributions to the state estimation, three auxiliary matrices are introduced with respect to the correlation between the state estimation errors and the biases, which helps to derive mode-conditioned estimates in the framework of the IMM. A numerical example involving tracking a maneuvering target is provided to compare the performance of the proposed filter with that of the augmented state filter.

KW - Interacting multiple model

KW - Jump Markov linear system

KW - Uncompensated bias

UR - http://www.scopus.com/inward/record.url?scp=84883515328&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84883515328

SN - 9781479901777

T3 - Proceedings of the American Control Conference

SP - 4903

EP - 4908

BT - 2013 American Control Conference, ACC 2013

T2 - 2013 1st American Control Conference, ACC 2013

Y2 - 17 June 2013 through 19 June 2013

ER -

State estimation for jump Markov linear systems with uncompensated biases

Abstract

Publication series

Conference

Keywords

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Cite this