Constrained Best Linear and Widely Linear Unbiased Estimation

Oliver Lang; Alexander Onic; Markus Steindl; Mario Huemer

doi:10.1109/ACSSC.2018.8645155

Constrained Best Linear and Widely Linear Unbiased Estimation

Oliver Lang, Alexander Onic, Markus Steindl, Mario Huemer

Institute of Signal Processing

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

Abstract

The least squares estimator (LSE) and the best linear unbiased estimator (BLUE) are two well-studied approaches for the estimation of deterministic but unknown parameters. In situations where the parameter vector is subject to linear constraints, the constrained LSE can be employed. In this paper, we derive the constrained version of the BLUE. In fact, two versions of the constrained BLUE are discussed, one of them with even weaker prerequisites than required for the well-known constrained LSE. In addition, the two corresponding versions of the constrained best widely linear unbiased estimator are presented.

Original language	English
Title of host publication	Proceedings of the Asilomar Conference on Signals, Systems, and Computers (ACSSC 2018)
Publisher	IEEE
Pages	1748-1752
Number of pages	5
ISBN (Print)	978-1-5386-9218-9
DOIs	https://doi.org/10.1109/ACSSC.2018.8645155
Publication status	Published - Oct 2018

Fields of science

202 Electrical Engineering, Electronics, Information Engineering
202015 Electronics
202022 Information technology
202037 Signal processing

JKU Focus areas

Computation in Informatics and Mathematics
Mechatronics and Information Processing

Access to Document

10.1109/ACSSC.2018.8645155

Cite this

@inproceedings{ffcf83b4735842f49c517d000427d77d,

title = "Constrained Best Linear and Widely Linear Unbiased Estimation",

abstract = "The least squares estimator (LSE) and the best linear unbiased estimator (BLUE) are two well-studied approaches for the estimation of deterministic but unknown parameters. In situations where the parameter vector is subject to linear constraints, the constrained LSE can be employed. In this paper, we derive the constrained version of the BLUE. In fact, two versions of the constrained BLUE are discussed, one of them with even weaker prerequisites than required for the well-known constrained LSE. In addition, the two corresponding versions of the constrained best widely linear unbiased estimator are presented.",

author = "Oliver Lang and Alexander Onic and Markus Steindl and Mario Huemer",

year = "2018",

month = oct,

doi = "10.1109/ACSSC.2018.8645155",

language = "English",

isbn = "978-1-5386-9218-9",

pages = "1748--1752",

booktitle = "Proceedings of the Asilomar Conference on Signals, Systems, and Computers (ACSSC 2018)",

publisher = "IEEE",

}

TY - GEN

T1 - Constrained Best Linear and Widely Linear Unbiased Estimation

AU - Lang, Oliver

AU - Onic, Alexander

AU - Steindl, Markus

AU - Huemer, Mario

PY - 2018/10

Y1 - 2018/10

N2 - The least squares estimator (LSE) and the best linear unbiased estimator (BLUE) are two well-studied approaches for the estimation of deterministic but unknown parameters. In situations where the parameter vector is subject to linear constraints, the constrained LSE can be employed. In this paper, we derive the constrained version of the BLUE. In fact, two versions of the constrained BLUE are discussed, one of them with even weaker prerequisites than required for the well-known constrained LSE. In addition, the two corresponding versions of the constrained best widely linear unbiased estimator are presented.

AB - The least squares estimator (LSE) and the best linear unbiased estimator (BLUE) are two well-studied approaches for the estimation of deterministic but unknown parameters. In situations where the parameter vector is subject to linear constraints, the constrained LSE can be employed. In this paper, we derive the constrained version of the BLUE. In fact, two versions of the constrained BLUE are discussed, one of them with even weaker prerequisites than required for the well-known constrained LSE. In addition, the two corresponding versions of the constrained best widely linear unbiased estimator are presented.

UR - https://ieeexplore.ieee.org/document/8645155

U2 - 10.1109/ACSSC.2018.8645155

DO - 10.1109/ACSSC.2018.8645155

M3 - Conference proceedings

SN - 978-1-5386-9218-9

SP - 1748

EP - 1752

BT - Proceedings of the Asilomar Conference on Signals, Systems, and Computers (ACSSC 2018)

PB - IEEE

ER -

Constrained Best Linear and Widely Linear Unbiased Estimation

Abstract

Fields of science

JKU Focus areas

Access to Document

Other files and links

Knowledge-Aided Methods in Estimation Theory

Cite this

Constrained Best Linear and Widely Linear Unbiased Estimation

Abstract

Fields of science

JKU Focus areas

Access to Document

Other files and links

Projects

Knowledge-Aided Methods in Estimation Theory

Cite this