Scattering framework for backwards partitioned estimators

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

A comprehensive framework for the study of backwards partitioned estimation problems is developed. An earlier work on providing a scattering framework for least-squares state-space estimation theory is extended to a more generalized case so as to handle a generalized backwards partitioning filter, by considering a fictitious initial layer between the actual initial layer and the primary one. The resulting algorithms are shown to be related to many other estimation algorithms, for example, a generalized two-filter smoothing algorithm, the Weinert-Desai smoothing algorithm, and generalized Chandrasekhar algorithms that are applicable to time-varying models as well as time-invariant ones for the filtering and smoothing problems.

Original languageEnglish
Pages (from-to)553-572
Number of pages20
JournalInternational Journal of Systems Science
Volume16
Issue number5
DOIs
Publication statusPublished - 1985
Externally publishedYes

Fingerprint

Smoothing Algorithm
Scattering
Filter
Estimator
Estimation Theory
Estimation Algorithms
Least Squares
Smoothing
Partitioning
Time-varying
State Space
Filtering
Invariant
Framework
Model

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

Scattering framework for backwards partitioned estimators. / Watanabe, Keigo.

In: International Journal of Systems Science, Vol. 16, No. 5, 1985, p. 553-572.

Research output: Contribution to journalArticle

@article{85e5a56dd5424d5b9495633bb6cd88b7,
title = "Scattering framework for backwards partitioned estimators",
abstract = "A comprehensive framework for the study of backwards partitioned estimation problems is developed. An earlier work on providing a scattering framework for least-squares state-space estimation theory is extended to a more generalized case so as to handle a generalized backwards partitioning filter, by considering a fictitious initial layer between the actual initial layer and the primary one. The resulting algorithms are shown to be related to many other estimation algorithms, for example, a generalized two-filter smoothing algorithm, the Weinert-Desai smoothing algorithm, and generalized Chandrasekhar algorithms that are applicable to time-varying models as well as time-invariant ones for the filtering and smoothing problems.",
author = "Keigo Watanabe",
year = "1985",
doi = "10.1080/00207728508926694",
language = "English",
volume = "16",
pages = "553--572",
journal = "International Journal of Systems Science",
issn = "0020-7721",
publisher = "Taylor and Francis Ltd.",
number = "5",

}

TY - JOUR

T1 - Scattering framework for backwards partitioned estimators

AU - Watanabe, Keigo

PY - 1985

Y1 - 1985

N2 - A comprehensive framework for the study of backwards partitioned estimation problems is developed. An earlier work on providing a scattering framework for least-squares state-space estimation theory is extended to a more generalized case so as to handle a generalized backwards partitioning filter, by considering a fictitious initial layer between the actual initial layer and the primary one. The resulting algorithms are shown to be related to many other estimation algorithms, for example, a generalized two-filter smoothing algorithm, the Weinert-Desai smoothing algorithm, and generalized Chandrasekhar algorithms that are applicable to time-varying models as well as time-invariant ones for the filtering and smoothing problems.

AB - A comprehensive framework for the study of backwards partitioned estimation problems is developed. An earlier work on providing a scattering framework for least-squares state-space estimation theory is extended to a more generalized case so as to handle a generalized backwards partitioning filter, by considering a fictitious initial layer between the actual initial layer and the primary one. The resulting algorithms are shown to be related to many other estimation algorithms, for example, a generalized two-filter smoothing algorithm, the Weinert-Desai smoothing algorithm, and generalized Chandrasekhar algorithms that are applicable to time-varying models as well as time-invariant ones for the filtering and smoothing problems.

UR - http://www.scopus.com/inward/record.url?scp=0022061448&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0022061448&partnerID=8YFLogxK

U2 - 10.1080/00207728508926694

DO - 10.1080/00207728508926694

M3 - Article

AN - SCOPUS:0022061448

VL - 16

SP - 553

EP - 572

JO - International Journal of Systems Science

JF - International Journal of Systems Science

SN - 0020-7721

IS - 5

ER -