Interacting multiple models algorithm based on α-β & α-β-γ filters

Yan Liang; Quan Pan; Hongcai Zhang

Interacting multiple models algorithm based on α-β & α-β-γ filters

Yan Liang, Quan Pan, Hongcai Zhang

School of Automation

Northwestern Polytechnical University Xian

Research output: Contribution to journal › Article › peer-review

Abstract

In this paper, we firstly prove in interacting multiple models algorithm (IMM) model-conditional estimational variances can be decoupled with model-conditional state estimation under certain conditions, and then supply the conditions to partly-decoupling and absolutely-decoupling among model-conditional estimational variances. Therefore IMM based on α-β α-β-γ filters is proposed. The Monte Carlo simulations show that this algorithm not only remains almost as accurate as IMM, but also save about half of the computation.

Original language	English
Journal	Kongzhi Lilun Yu Yingyong/Control Theory and Applications
Volume	16
Issue number	5
State	Published - 1999

Keywords

α-β filter
α-β-γ filter
Adaptive filtering
Tracking

Cite this

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title = "Interacting multiple models algorithm based on α-β & α-β-γ filters",

abstract = "In this paper, we firstly prove in interacting multiple models algorithm (IMM) model-conditional estimational variances can be decoupled with model-conditional state estimation under certain conditions, and then supply the conditions to partly-decoupling and absolutely-decoupling among model-conditional estimational variances. Therefore IMM based on α-β α-β-γ filters is proposed. The Monte Carlo simulations show that this algorithm not only remains almost as accurate as IMM, but also save about half of the computation.",

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author = "Yan Liang and Quan Pan and Hongcai Zhang",

year = "1999",

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journal = "Kongzhi Lilun Yu Yingyong/Control Theory and Applications",

issn = "1000-8152",

publisher = "South China University of Technology",

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T1 - Interacting multiple models algorithm based on α-β & α-β-γ filters

AU - Liang, Yan

AU - Pan, Quan

AU - Zhang, Hongcai

PY - 1999

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N2 - In this paper, we firstly prove in interacting multiple models algorithm (IMM) model-conditional estimational variances can be decoupled with model-conditional state estimation under certain conditions, and then supply the conditions to partly-decoupling and absolutely-decoupling among model-conditional estimational variances. Therefore IMM based on α-β α-β-γ filters is proposed. The Monte Carlo simulations show that this algorithm not only remains almost as accurate as IMM, but also save about half of the computation.

AB - In this paper, we firstly prove in interacting multiple models algorithm (IMM) model-conditional estimational variances can be decoupled with model-conditional state estimation under certain conditions, and then supply the conditions to partly-decoupling and absolutely-decoupling among model-conditional estimational variances. Therefore IMM based on α-β α-β-γ filters is proposed. The Monte Carlo simulations show that this algorithm not only remains almost as accurate as IMM, but also save about half of the computation.

KW - α-β filter

KW - α-β-γ filter

KW - Adaptive filtering

KW - Tracking

UR - http://www.scopus.com/inward/record.url?scp=76549114722&partnerID=8YFLogxK

M3 - 文章

AN - SCOPUS:76549114722

SN - 1000-8152

VL - 16

JO - Kongzhi Lilun Yu Yingyong/Control Theory and Applications

JF - Kongzhi Lilun Yu Yingyong/Control Theory and Applications

IS - 5

ER -

Interacting multiple models algorithm based on α-β & α-β-γ filters

Abstract

Keywords

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