Convergence analysis of consensus belief functions within asynchronous ad-hoc fusion networks

Thanuka L. Wickramrathne, Kamal Premaratne, Manohar N. Murthiy

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

5 Citations (Scopus)

Abstract

In a multi-agent data fusion scenario, agents may iteratively exchange their states to arrive at a consensus state which signifies 'general agreement' among the agents. Agent states that are being exchanged may have been generated from hard (i.e., physics based) or soft (i.e., human based evidence. such as opinions or beliefs regarding an event) sensors. Convergence analysis becomes an extremely challenging problem in such complex fusion environments, which may involve communication delays, ad-hoc paths, etc. In this paper, we analyze consensus of a Dempster-Shafer theoretic (DST) fusion operator by formulating the consensus problem as finding common fixed points of a pool of paracontracting operators. Due to its DST basis, this consensus protocol can deal with a wider variety of data imperfections characteristic of hard+soft data fusion environments. It also easily adapts itself to networks where agent states are captured with probability mass functions because they can be considered a special case of DST models.

Original languageEnglish
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Pages3612-3616
Number of pages5
DOIs
StatePublished - Oct 18 2013
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: May 26 2013May 31 2013

Other

Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
CountryCanada
CityVancouver, BC
Period5/26/135/31/13

Fingerprint

Data fusion
Physics
Defects
Communication
Sensors

Keywords

  • Consensus
  • data fusion
  • Dempster-Shafer theory
  • multi-agent systems
  • paracontracting operators

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

Cite this

Wickramrathne, T. L., Premaratne, K., & Murthiy, M. N. (2013). Convergence analysis of consensus belief functions within asynchronous ad-hoc fusion networks. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (pp. 3612-3616). [6638331] https://doi.org/10.1109/ICASSP.2013.6638331

Convergence analysis of consensus belief functions within asynchronous ad-hoc fusion networks. / Wickramrathne, Thanuka L.; Premaratne, Kamal; Murthiy, Manohar N.

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2013. p. 3612-3616 6638331.

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

Wickramrathne, TL, Premaratne, K & Murthiy, MN 2013, Convergence analysis of consensus belief functions within asynchronous ad-hoc fusion networks. in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings., 6638331, pp. 3612-3616, 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013, Vancouver, BC, Canada, 5/26/13. https://doi.org/10.1109/ICASSP.2013.6638331
Wickramrathne TL, Premaratne K, Murthiy MN. Convergence analysis of consensus belief functions within asynchronous ad-hoc fusion networks. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2013. p. 3612-3616. 6638331 https://doi.org/10.1109/ICASSP.2013.6638331
Wickramrathne, Thanuka L. ; Premaratne, Kamal ; Murthiy, Manohar N. / Convergence analysis of consensus belief functions within asynchronous ad-hoc fusion networks. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2013. pp. 3612-3616
@inproceedings{d4a1789a3ebf40c19c85c2e9b010dd6b,
title = "Convergence analysis of consensus belief functions within asynchronous ad-hoc fusion networks",
abstract = "In a multi-agent data fusion scenario, agents may iteratively exchange their states to arrive at a consensus state which signifies 'general agreement' among the agents. Agent states that are being exchanged may have been generated from hard (i.e., physics based) or soft (i.e., human based evidence. such as opinions or beliefs regarding an event) sensors. Convergence analysis becomes an extremely challenging problem in such complex fusion environments, which may involve communication delays, ad-hoc paths, etc. In this paper, we analyze consensus of a Dempster-Shafer theoretic (DST) fusion operator by formulating the consensus problem as finding common fixed points of a pool of paracontracting operators. Due to its DST basis, this consensus protocol can deal with a wider variety of data imperfections characteristic of hard+soft data fusion environments. It also easily adapts itself to networks where agent states are captured with probability mass functions because they can be considered a special case of DST models.",
keywords = "Consensus, data fusion, Dempster-Shafer theory, multi-agent systems, paracontracting operators",
author = "Wickramrathne, {Thanuka L.} and Kamal Premaratne and Murthiy, {Manohar N.}",
year = "2013",
month = "10",
day = "18",
doi = "10.1109/ICASSP.2013.6638331",
language = "English",
isbn = "9781479903566",
pages = "3612--3616",
booktitle = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

}

TY - GEN

T1 - Convergence analysis of consensus belief functions within asynchronous ad-hoc fusion networks

AU - Wickramrathne, Thanuka L.

AU - Premaratne, Kamal

AU - Murthiy, Manohar N.

PY - 2013/10/18

Y1 - 2013/10/18

N2 - In a multi-agent data fusion scenario, agents may iteratively exchange their states to arrive at a consensus state which signifies 'general agreement' among the agents. Agent states that are being exchanged may have been generated from hard (i.e., physics based) or soft (i.e., human based evidence. such as opinions or beliefs regarding an event) sensors. Convergence analysis becomes an extremely challenging problem in such complex fusion environments, which may involve communication delays, ad-hoc paths, etc. In this paper, we analyze consensus of a Dempster-Shafer theoretic (DST) fusion operator by formulating the consensus problem as finding common fixed points of a pool of paracontracting operators. Due to its DST basis, this consensus protocol can deal with a wider variety of data imperfections characteristic of hard+soft data fusion environments. It also easily adapts itself to networks where agent states are captured with probability mass functions because they can be considered a special case of DST models.

AB - In a multi-agent data fusion scenario, agents may iteratively exchange their states to arrive at a consensus state which signifies 'general agreement' among the agents. Agent states that are being exchanged may have been generated from hard (i.e., physics based) or soft (i.e., human based evidence. such as opinions or beliefs regarding an event) sensors. Convergence analysis becomes an extremely challenging problem in such complex fusion environments, which may involve communication delays, ad-hoc paths, etc. In this paper, we analyze consensus of a Dempster-Shafer theoretic (DST) fusion operator by formulating the consensus problem as finding common fixed points of a pool of paracontracting operators. Due to its DST basis, this consensus protocol can deal with a wider variety of data imperfections characteristic of hard+soft data fusion environments. It also easily adapts itself to networks where agent states are captured with probability mass functions because they can be considered a special case of DST models.

KW - Consensus

KW - data fusion

KW - Dempster-Shafer theory

KW - multi-agent systems

KW - paracontracting operators

UR - http://www.scopus.com/inward/record.url?scp=84890514038&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84890514038&partnerID=8YFLogxK

U2 - 10.1109/ICASSP.2013.6638331

DO - 10.1109/ICASSP.2013.6638331

M3 - Conference contribution

SN - 9781479903566

SP - 3612

EP - 3616

BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

ER -