Continuous-time decentralized smoothers based on two-filter form: Identical local and global models

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

This paper proposes some decentralized smoothing algorithms for a continuous-time linear estimation structure consisting of a central processor and of two local processors, in which the local models are assumed to be identical to the global model. The philosophy of the paper is to solve the problem in terms of the local forward and backward information (or Kalman) filters. The resulting algorithms are somewhat different from those based on the local smoothing estimates which have been studied by some other authors. Smoothing update and real-time smoothing algorithms are also presented, ft is shown that the present algorithms have some advantages: the global filtered estimates can be obtained in the course of computing the decentralized smoothing estimates and the central and local processors can be derived in a completely parallel fashion.

Original languageEnglish
Pages (from-to)1015-1028
Number of pages14
JournalInternational Journal of Systems Science
Volume17
Issue number7
DOIs
Publication statusPublished - 1986
Externally publishedYes

Fingerprint

Decentralized
Continuous Time
Filter
Smoothing Algorithm
Smoothing
Estimate
Local Smoothing
Linear Estimation
Kalman filters
Model
Kalman Filter
Update
Real-time
Form
Global model
Continuous time
Computing

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Management Science and Operations Research

Cite this

@article{a6c40c8990ff45acb590875f6d31899c,
title = "Continuous-time decentralized smoothers based on two-filter form: Identical local and global models",
abstract = "This paper proposes some decentralized smoothing algorithms for a continuous-time linear estimation structure consisting of a central processor and of two local processors, in which the local models are assumed to be identical to the global model. The philosophy of the paper is to solve the problem in terms of the local forward and backward information (or Kalman) filters. The resulting algorithms are somewhat different from those based on the local smoothing estimates which have been studied by some other authors. Smoothing update and real-time smoothing algorithms are also presented, ft is shown that the present algorithms have some advantages: the global filtered estimates can be obtained in the course of computing the decentralized smoothing estimates and the central and local processors can be derived in a completely parallel fashion.",
author = "Keigo Watanabe",
year = "1986",
doi = "10.1080/00207728608926866",
language = "English",
volume = "17",
pages = "1015--1028",
journal = "International Journal of Systems Science",
issn = "0020-7721",
publisher = "Taylor and Francis Ltd.",
number = "7",

}

TY - JOUR

T1 - Continuous-time decentralized smoothers based on two-filter form

T2 - Identical local and global models

AU - Watanabe, Keigo

PY - 1986

Y1 - 1986

N2 - This paper proposes some decentralized smoothing algorithms for a continuous-time linear estimation structure consisting of a central processor and of two local processors, in which the local models are assumed to be identical to the global model. The philosophy of the paper is to solve the problem in terms of the local forward and backward information (or Kalman) filters. The resulting algorithms are somewhat different from those based on the local smoothing estimates which have been studied by some other authors. Smoothing update and real-time smoothing algorithms are also presented, ft is shown that the present algorithms have some advantages: the global filtered estimates can be obtained in the course of computing the decentralized smoothing estimates and the central and local processors can be derived in a completely parallel fashion.

AB - This paper proposes some decentralized smoothing algorithms for a continuous-time linear estimation structure consisting of a central processor and of two local processors, in which the local models are assumed to be identical to the global model. The philosophy of the paper is to solve the problem in terms of the local forward and backward information (or Kalman) filters. The resulting algorithms are somewhat different from those based on the local smoothing estimates which have been studied by some other authors. Smoothing update and real-time smoothing algorithms are also presented, ft is shown that the present algorithms have some advantages: the global filtered estimates can be obtained in the course of computing the decentralized smoothing estimates and the central and local processors can be derived in a completely parallel fashion.

UR - http://www.scopus.com/inward/record.url?scp=0022756121&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0022756121&partnerID=8YFLogxK

U2 - 10.1080/00207728608926866

DO - 10.1080/00207728608926866

M3 - Article

AN - SCOPUS:0022756121

VL - 17

SP - 1015

EP - 1028

JO - International Journal of Systems Science

JF - International Journal of Systems Science

SN - 0020-7721

IS - 7

ER -