Evidence filtering

Duminda A. Dewasurendra, Peter H. Bauer, Kamal Premaratne

Research output: Contribution to journalArticle

32 Citations (Scopus)

Abstract

A novel framework named evidence filtering for processing information from multiple sensor modalities is presented. This approach is based on conditional belief notions in Dempster-Shafer (DS) evidence theory and enables one to directly process temporally and spatially distributed sensor data and infer on the "frequency" characteristics of various events of interest. The method can accommodate partial and incomplete information from multiple sensor modalities during the process. Certain restrictions on the coefficients impose several challenges in the design of evidence filters suggesting that arbitrary frequency shaping is not possible. A design procedure and the analysis of nonrecursive evidence filters is presented. A threat assessment scenario is simulated and the results are presented to illustrate the applications of evidence filtering.

Original languageEnglish
Pages (from-to)5796-5805
Number of pages10
JournalIEEE Transactions on Signal Processing
Volume55
Issue number12
DOIs
StatePublished - Dec 1 2007

Fingerprint

Sensors

Keywords

  • Dempster-Shafer (DS) belief theory
  • Evidence filters
  • Filter design
  • Inference mechanisms
  • Partial and incomplete information
  • Positive systems
  • Sensor modalities
  • Signature detection
  • Spatiotemporal sensor data
  • Uncertainty

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing

Cite this

Evidence filtering. / Dewasurendra, Duminda A.; Bauer, Peter H.; Premaratne, Kamal.

In: IEEE Transactions on Signal Processing, Vol. 55, No. 12, 01.12.2007, p. 5796-5805.

Research output: Contribution to journalArticle

Dewasurendra, Duminda A. ; Bauer, Peter H. ; Premaratne, Kamal. / Evidence filtering. In: IEEE Transactions on Signal Processing. 2007 ; Vol. 55, No. 12. pp. 5796-5805.
@article{d8d8f3b5df9b404d86e2a1d4c236192e,
title = "Evidence filtering",
abstract = "A novel framework named evidence filtering for processing information from multiple sensor modalities is presented. This approach is based on conditional belief notions in Dempster-Shafer (DS) evidence theory and enables one to directly process temporally and spatially distributed sensor data and infer on the {"}frequency{"} characteristics of various events of interest. The method can accommodate partial and incomplete information from multiple sensor modalities during the process. Certain restrictions on the coefficients impose several challenges in the design of evidence filters suggesting that arbitrary frequency shaping is not possible. A design procedure and the analysis of nonrecursive evidence filters is presented. A threat assessment scenario is simulated and the results are presented to illustrate the applications of evidence filtering.",
keywords = "Dempster-Shafer (DS) belief theory, Evidence filters, Filter design, Inference mechanisms, Partial and incomplete information, Positive systems, Sensor modalities, Signature detection, Spatiotemporal sensor data, Uncertainty",
author = "Dewasurendra, {Duminda A.} and Bauer, {Peter H.} and Kamal Premaratne",
year = "2007",
month = "12",
day = "1",
doi = "10.1109/TSP.2007.900759",
language = "English",
volume = "55",
pages = "5796--5805",
journal = "IEEE Transactions on Signal Processing",
issn = "1053-587X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "12",

}

TY - JOUR

T1 - Evidence filtering

AU - Dewasurendra, Duminda A.

AU - Bauer, Peter H.

AU - Premaratne, Kamal

PY - 2007/12/1

Y1 - 2007/12/1

N2 - A novel framework named evidence filtering for processing information from multiple sensor modalities is presented. This approach is based on conditional belief notions in Dempster-Shafer (DS) evidence theory and enables one to directly process temporally and spatially distributed sensor data and infer on the "frequency" characteristics of various events of interest. The method can accommodate partial and incomplete information from multiple sensor modalities during the process. Certain restrictions on the coefficients impose several challenges in the design of evidence filters suggesting that arbitrary frequency shaping is not possible. A design procedure and the analysis of nonrecursive evidence filters is presented. A threat assessment scenario is simulated and the results are presented to illustrate the applications of evidence filtering.

AB - A novel framework named evidence filtering for processing information from multiple sensor modalities is presented. This approach is based on conditional belief notions in Dempster-Shafer (DS) evidence theory and enables one to directly process temporally and spatially distributed sensor data and infer on the "frequency" characteristics of various events of interest. The method can accommodate partial and incomplete information from multiple sensor modalities during the process. Certain restrictions on the coefficients impose several challenges in the design of evidence filters suggesting that arbitrary frequency shaping is not possible. A design procedure and the analysis of nonrecursive evidence filters is presented. A threat assessment scenario is simulated and the results are presented to illustrate the applications of evidence filtering.

KW - Dempster-Shafer (DS) belief theory

KW - Evidence filters

KW - Filter design

KW - Inference mechanisms

KW - Partial and incomplete information

KW - Positive systems

KW - Sensor modalities

KW - Signature detection

KW - Spatiotemporal sensor data

KW - Uncertainty

UR - http://www.scopus.com/inward/record.url?scp=36749068936&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=36749068936&partnerID=8YFLogxK

U2 - 10.1109/TSP.2007.900759

DO - 10.1109/TSP.2007.900759

M3 - Article

VL - 55

SP - 5796

EP - 5805

JO - IEEE Transactions on Signal Processing

JF - IEEE Transactions on Signal Processing

SN - 1053-587X

IS - 12

ER -