Unified optimal linear estimation fusion. II. Discussions and examples

X. Rong Li; Jie Wang

doi:10.1109/IFIC.2000.862452

Unified optimal linear estimation fusion. II. Discussions and examples

X. Rong Li
, Jie Wang

University of New Orleans
Xi'an Jiaotong University

Research output: Contribution to conference › Paper › peer-review

23 Scopus citations

Abstract

Several unified optimal estimation/track fusion rules in the sense of best linear unbiased estimation (BLUE) and weighted least squares (WLS) have been presented in pt.I for centralized, distributed, and hybrid fusion architectures. This paper discusses their pros and cons, verifies these rules, and demonstrate via simulation examples how these fusion rules can be used in cases with either complete, incomplete, or no prior information about the estimate (i.e., the quantity to be estimated).

Original language	English
Pages	MoC218-MoC225
DOIs	https://doi.org/10.1109/IFIC.2000.862452
State	Published - 2000
Externally published	Yes
Event	3rd International Conference on Information Fusion, FUSION 2000 - Paris, France Duration: 10 Jul 2000 → 13 Jul 2000

Conference

Conference	3rd International Conference on Information Fusion, FUSION 2000
Country/Territory	France
City	Paris
Period	10/07/00 → 13/07/00

Keywords

BLUE
Estimation fusion
MMSE estimation
fusion rules
least squares
track fusion

Access to Document

10.1109/IFIC.2000.862452

Cite this

@conference{8185434e8f1c4e70bf9e4134d6e9542e,

title = "Unified optimal linear estimation fusion. II. Discussions and examples",

abstract = "Several unified optimal estimation/track fusion rules in the sense of best linear unbiased estimation (BLUE) and weighted least squares (WLS) have been presented in pt.I for centralized, distributed, and hybrid fusion architectures. This paper discusses their pros and cons, verifies these rules, and demonstrate via simulation examples how these fusion rules can be used in cases with either complete, incomplete, or no prior information about the estimate (i.e., the quantity to be estimated).",

keywords = "BLUE, Estimation fusion, MMSE estimation, fusion rules, least squares, track fusion",

author = "Li, \{X. Rong\} and Jie Wang",

year = "2000",

doi = "10.1109/IFIC.2000.862452",

language = "英语",

pages = "MoC218--MoC225",

note = "3rd International Conference on Information Fusion, FUSION 2000 ; Conference date: 10-07-2000 Through 13-07-2000",

}

TY - CONF

T1 - Unified optimal linear estimation fusion. II. Discussions and examples

AU - Li, X. Rong

AU - Wang, Jie

PY - 2000

Y1 - 2000

N2 - Several unified optimal estimation/track fusion rules in the sense of best linear unbiased estimation (BLUE) and weighted least squares (WLS) have been presented in pt.I for centralized, distributed, and hybrid fusion architectures. This paper discusses their pros and cons, verifies these rules, and demonstrate via simulation examples how these fusion rules can be used in cases with either complete, incomplete, or no prior information about the estimate (i.e., the quantity to be estimated).

AB - Several unified optimal estimation/track fusion rules in the sense of best linear unbiased estimation (BLUE) and weighted least squares (WLS) have been presented in pt.I for centralized, distributed, and hybrid fusion architectures. This paper discusses their pros and cons, verifies these rules, and demonstrate via simulation examples how these fusion rules can be used in cases with either complete, incomplete, or no prior information about the estimate (i.e., the quantity to be estimated).

KW - BLUE

KW - Estimation fusion

KW - MMSE estimation

KW - fusion rules

KW - least squares

KW - track fusion

UR - https://www.scopus.com/pages/publications/84901451703

U2 - 10.1109/IFIC.2000.862452

DO - 10.1109/IFIC.2000.862452

M3 - 论文

AN - SCOPUS:84901451703

SP - MoC218-MoC225

T2 - 3rd International Conference on Information Fusion, FUSION 2000

Y2 - 10 July 2000 through 13 July 2000

ER -

Unified optimal linear estimation fusion. II. Discussions and examples

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this