Non-linear least squares estimation via network gossiping

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Various estimation problems can be formulated as non-linear least squares (NLLS) problems, which can be solved using the Gauss-Newton algorithm. In this paper, we use gossiping to implement the Gauss-Newton algorithm in a fully distributed fashion, and show the convergence of this Gossip-based Gauss-Newton (GGN) algorithm. As an example, we show by simulations that the GGN algorithm is effective and robust in solving power system state estimation, and that the Mean Square Error (MSE) performance remains comparable to the centralized scheme and degrades gracefully even with random link/node failures.

Original languageEnglish (US)
Title of host publicationConference Record - Asilomar Conference on Signals, Systems and Computers
Pages1508-1512
Number of pages5
DOIs
StatePublished - 2012
Externally publishedYes
Event46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012 - Pacific Grove, CA, United States
Duration: Nov 4 2012Nov 7 2012

Other

Other46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012
CountryUnited States
CityPacific Grove, CA
Period11/4/1211/7/12

Fingerprint

State estimation
Mean square error

Keywords

  • convergence
  • gossiping
  • least squares estimation

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing

Cite this

Li, X., & Scaglione, A. (2012). Non-linear least squares estimation via network gossiping. In Conference Record - Asilomar Conference on Signals, Systems and Computers (pp. 1508-1512). [6489279] https://doi.org/10.1109/ACSSC.2012.6489279

Non-linear least squares estimation via network gossiping. / Li, Xiao; Scaglione, Anna.

Conference Record - Asilomar Conference on Signals, Systems and Computers. 2012. p. 1508-1512 6489279.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Li, X & Scaglione, A 2012, Non-linear least squares estimation via network gossiping. in Conference Record - Asilomar Conference on Signals, Systems and Computers., 6489279, pp. 1508-1512, 46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012, Pacific Grove, CA, United States, 11/4/12. https://doi.org/10.1109/ACSSC.2012.6489279
Li X, Scaglione A. Non-linear least squares estimation via network gossiping. In Conference Record - Asilomar Conference on Signals, Systems and Computers. 2012. p. 1508-1512. 6489279 https://doi.org/10.1109/ACSSC.2012.6489279
Li, Xiao ; Scaglione, Anna. / Non-linear least squares estimation via network gossiping. Conference Record - Asilomar Conference on Signals, Systems and Computers. 2012. pp. 1508-1512
@inproceedings{56853d13c15d431b9e810d05a2a8a90b,
title = "Non-linear least squares estimation via network gossiping",
abstract = "Various estimation problems can be formulated as non-linear least squares (NLLS) problems, which can be solved using the Gauss-Newton algorithm. In this paper, we use gossiping to implement the Gauss-Newton algorithm in a fully distributed fashion, and show the convergence of this Gossip-based Gauss-Newton (GGN) algorithm. As an example, we show by simulations that the GGN algorithm is effective and robust in solving power system state estimation, and that the Mean Square Error (MSE) performance remains comparable to the centralized scheme and degrades gracefully even with random link/node failures.",
keywords = "convergence, gossiping, least squares estimation",
author = "Xiao Li and Anna Scaglione",
year = "2012",
doi = "10.1109/ACSSC.2012.6489279",
language = "English (US)",
isbn = "9781467350518",
pages = "1508--1512",
booktitle = "Conference Record - Asilomar Conference on Signals, Systems and Computers",

}

TY - GEN

T1 - Non-linear least squares estimation via network gossiping

AU - Li, Xiao

AU - Scaglione, Anna

PY - 2012

Y1 - 2012

N2 - Various estimation problems can be formulated as non-linear least squares (NLLS) problems, which can be solved using the Gauss-Newton algorithm. In this paper, we use gossiping to implement the Gauss-Newton algorithm in a fully distributed fashion, and show the convergence of this Gossip-based Gauss-Newton (GGN) algorithm. As an example, we show by simulations that the GGN algorithm is effective and robust in solving power system state estimation, and that the Mean Square Error (MSE) performance remains comparable to the centralized scheme and degrades gracefully even with random link/node failures.

AB - Various estimation problems can be formulated as non-linear least squares (NLLS) problems, which can be solved using the Gauss-Newton algorithm. In this paper, we use gossiping to implement the Gauss-Newton algorithm in a fully distributed fashion, and show the convergence of this Gossip-based Gauss-Newton (GGN) algorithm. As an example, we show by simulations that the GGN algorithm is effective and robust in solving power system state estimation, and that the Mean Square Error (MSE) performance remains comparable to the centralized scheme and degrades gracefully even with random link/node failures.

KW - convergence

KW - gossiping

KW - least squares estimation

UR - http://www.scopus.com/inward/record.url?scp=84876216097&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84876216097&partnerID=8YFLogxK

U2 - 10.1109/ACSSC.2012.6489279

DO - 10.1109/ACSSC.2012.6489279

M3 - Conference contribution

AN - SCOPUS:84876216097

SN - 9781467350518

SP - 1508

EP - 1512

BT - Conference Record - Asilomar Conference on Signals, Systems and Computers

ER -