Multisensor statistical interval estimation fusion

Yunmin Zhu; Yu Gan; X. Rong Li

doi:10.1117/12.458392

Multisensor statistical interval estimation fusion

Yunmin Zhu
, Yu Gan
, X. Rong Li

Sichuan University

Research output: Contribution to journal › Conference article › peer-review

1 Scopus citations

Abstract

This paper deals with multisensor statistical interval interval estimation fusion, that is, data fusion from multiple statistical interval estimators for the purpose of estimation of a parameter θ. A multisensor convex linear statistic fusion model for optimal interval estimation fusion is established. A Gaussian-Seidel iteration algorithm for searching for the fusion weights is proposed. In particular, we suggest convex combination minimum variance fusion that, reduces huge computation of fusion weights and yields near optimal estimate performance generally, and moreover, may achieve exactly optimal performance for some specific distributions of obsevation data. Numerical examples are provided and give additional support to the above results.

Original language	English
Pages (from-to)	269-279
Number of pages	11
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	4731
DOIs	https://doi.org/10.1117/12.458392
State	Published - 2002
Externally published	Yes
Event	Sensor Fusion: Architectures, Algorithms, and Applications VI - Orlando, FL, United States Duration: 3 Apr 2002 → 5 Apr 2002

Keywords

Convex linear fusion
Coverage probability
Interval estimation
Least mean square

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10.1117/12.458392

Cite this

@article{5771fd4572d64d24993d0c493d711434,

title = "Multisensor statistical interval estimation fusion",

abstract = "This paper deals with multisensor statistical interval interval estimation fusion, that is, data fusion from multiple statistical interval estimators for the purpose of estimation of a parameter θ. A multisensor convex linear statistic fusion model for optimal interval estimation fusion is established. A Gaussian-Seidel iteration algorithm for searching for the fusion weights is proposed. In particular, we suggest convex combination minimum variance fusion that, reduces huge computation of fusion weights and yields near optimal estimate performance generally, and moreover, may achieve exactly optimal performance for some specific distributions of obsevation data. Numerical examples are provided and give additional support to the above results.",

keywords = "Convex linear fusion, Coverage probability, Interval estimation, Least mean square",

author = "Yunmin Zhu and Yu Gan and Li, \{X. Rong\}",

year = "2002",

doi = "10.1117/12.458392",

language = "英语",

volume = "4731",

pages = "269--279",

journal = "Proceedings of SPIE - The International Society for Optical Engineering",

issn = "0277-786X",

publisher = "SPIE",

note = "Sensor Fusion: Architectures, Algorithms, and Applications VI ; Conference date: 03-04-2002 Through 05-04-2002",

}

TY - JOUR

T1 - Multisensor statistical interval estimation fusion

AU - Zhu, Yunmin

AU - Gan, Yu

AU - Li, X. Rong

PY - 2002

Y1 - 2002

N2 - This paper deals with multisensor statistical interval interval estimation fusion, that is, data fusion from multiple statistical interval estimators for the purpose of estimation of a parameter θ. A multisensor convex linear statistic fusion model for optimal interval estimation fusion is established. A Gaussian-Seidel iteration algorithm for searching for the fusion weights is proposed. In particular, we suggest convex combination minimum variance fusion that, reduces huge computation of fusion weights and yields near optimal estimate performance generally, and moreover, may achieve exactly optimal performance for some specific distributions of obsevation data. Numerical examples are provided and give additional support to the above results.

AB - This paper deals with multisensor statistical interval interval estimation fusion, that is, data fusion from multiple statistical interval estimators for the purpose of estimation of a parameter θ. A multisensor convex linear statistic fusion model for optimal interval estimation fusion is established. A Gaussian-Seidel iteration algorithm for searching for the fusion weights is proposed. In particular, we suggest convex combination minimum variance fusion that, reduces huge computation of fusion weights and yields near optimal estimate performance generally, and moreover, may achieve exactly optimal performance for some specific distributions of obsevation data. Numerical examples are provided and give additional support to the above results.

KW - Convex linear fusion

KW - Coverage probability

KW - Interval estimation

KW - Least mean square

UR - https://www.scopus.com/pages/publications/0036384669

U2 - 10.1117/12.458392

DO - 10.1117/12.458392

M3 - 会议文章

AN - SCOPUS:0036384669

SN - 0277-786X

VL - 4731

SP - 269

EP - 279

JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

T2 - Sensor Fusion: Architectures, Algorithms, and Applications VI

Y2 - 3 April 2002 through 5 April 2002

ER -

Multisensor statistical interval estimation fusion

Abstract

Keywords

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