Distributed information consensus filters for simultaneous input and state estimation

Y. Lu, L Zhang, Xuerong Mao

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

This paper describes the distributed information filtering where a set of sensor networks are required to simultaneously estimate input and state of a linear discrete-time system from collaborative manner. Our research purpose is to develop a consensus strategy in which sensor nodes communicate within the network through a sequence of Kalman iterations and data diffusion. A novel recursive information filtering is proposed by integrating input estimation error into measurement data and weighted information matrices. On the fusing process, local system state filtering transmits estimation information using the consensus averaging algorithm, which penalizes the disagreement in a dynamic manner. A simulation example is provided to compare the performance of the distributed information filtering with optimal Gillijins–De Moor’s algorithm.
LanguageEnglish
Pages877-888
Number of pages12
JournalCircuits, Systems, and Signal Processing
Volume32
Issue number2
DOIs
Publication statusPublished - 1 Apr 2013

Fingerprint

Information filtering
Information Filtering
State Estimation
State estimation
Filter
Discrete-time Linear Systems
Local System
Information Matrix
Estimation Error
Measurement errors
Sensor nodes
Error analysis
Sensor networks
Sensor Networks
Averaging
Filtering
Iteration
Sensor
Vertex of a graph
Estimate

Keywords

  • unknown input estimation
  • distributed estimation
  • information filters
  • consensus
  • sensor networks

Cite this

@article{f10011e278c84e9884747e1a2ca7c5bf,
title = "Distributed information consensus filters for simultaneous input and state estimation",
abstract = "This paper describes the distributed information filtering where a set of sensor networks are required to simultaneously estimate input and state of a linear discrete-time system from collaborative manner. Our research purpose is to develop a consensus strategy in which sensor nodes communicate within the network through a sequence of Kalman iterations and data diffusion. A novel recursive information filtering is proposed by integrating input estimation error into measurement data and weighted information matrices. On the fusing process, local system state filtering transmits estimation information using the consensus averaging algorithm, which penalizes the disagreement in a dynamic manner. A simulation example is provided to compare the performance of the distributed information filtering with optimal Gillijins–De Moor’s algorithm.",
keywords = "unknown input estimation, distributed estimation, information filters, consensus, sensor networks",
author = "Y. Lu and L Zhang and Xuerong Mao",
year = "2013",
month = "4",
day = "1",
doi = "10.1007/s00034-012-9460-8",
language = "English",
volume = "32",
pages = "877--888",
journal = "Circuits, Systems, and Signal Processing",
issn = "1531-5878",
number = "2",

}

Distributed information consensus filters for simultaneous input and state estimation. / Lu, Y.; Zhang, L ; Mao, Xuerong.

In: Circuits, Systems, and Signal Processing, Vol. 32, No. 2, 01.04.2013, p. 877-888.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Distributed information consensus filters for simultaneous input and state estimation

AU - Lu, Y.

AU - Zhang, L

AU - Mao, Xuerong

PY - 2013/4/1

Y1 - 2013/4/1

N2 - This paper describes the distributed information filtering where a set of sensor networks are required to simultaneously estimate input and state of a linear discrete-time system from collaborative manner. Our research purpose is to develop a consensus strategy in which sensor nodes communicate within the network through a sequence of Kalman iterations and data diffusion. A novel recursive information filtering is proposed by integrating input estimation error into measurement data and weighted information matrices. On the fusing process, local system state filtering transmits estimation information using the consensus averaging algorithm, which penalizes the disagreement in a dynamic manner. A simulation example is provided to compare the performance of the distributed information filtering with optimal Gillijins–De Moor’s algorithm.

AB - This paper describes the distributed information filtering where a set of sensor networks are required to simultaneously estimate input and state of a linear discrete-time system from collaborative manner. Our research purpose is to develop a consensus strategy in which sensor nodes communicate within the network through a sequence of Kalman iterations and data diffusion. A novel recursive information filtering is proposed by integrating input estimation error into measurement data and weighted information matrices. On the fusing process, local system state filtering transmits estimation information using the consensus averaging algorithm, which penalizes the disagreement in a dynamic manner. A simulation example is provided to compare the performance of the distributed information filtering with optimal Gillijins–De Moor’s algorithm.

KW - unknown input estimation

KW - distributed estimation

KW - information filters

KW - consensus

KW - sensor networks

UR - http://link.springer.com/journal/34

U2 - 10.1007/s00034-012-9460-8

DO - 10.1007/s00034-012-9460-8

M3 - Article

VL - 32

SP - 877

EP - 888

JO - Circuits, Systems, and Signal Processing

T2 - Circuits, Systems, and Signal Processing

JF - Circuits, Systems, and Signal Processing

SN - 1531-5878

IS - 2

ER -