Hostname: page-component-848d4c4894-xfwgj Total loading time: 0 Render date: 2024-07-05T13:26:51.790Z Has data issue: false hasContentIssue false
  • English
  • Français

Dynamic estimation of evidence discounting rates based on information credibility

Published online by Cambridge University Press:  22 December 2010

M. C. Florea ,
A.-L. Jousselme  and
É. Bossé
M. C. Florea
Affiliation:
Thales Canada Inc., Land & Joint Systems Division, Québec, Canada. Mihai.Florea@ca.thalesgroup.com
A.-L. Jousselme
Affiliation:
Defence R & D Canada – Valcartier, Decision Support Systems for Command and Control Section, Québec, Canada. Anne-Laure.Jousselme@drdc-rddc.gc.ca; Eloi.Bosse@drdc-rddc.gc.ca
É. Bossé
Affiliation:
Defence R & D Canada – Valcartier, Decision Support Systems for Command and Control Section, Québec, Canada. Anne-Laure.Jousselme@drdc-rddc.gc.ca; Eloi.Bosse@drdc-rddc.gc.ca
Get access

Abstract

Information quality is crucial to any information fusion system as combining unreliable or partially credible pieces of information may lead to erroneous results. In this paper, Dempster-Shafer theory of evidence is being used as a framework for representing and combining uncertain pieces of information. We propose a method of dynamic estimation of evidence discounting rates based on the credibility of pieces of information. The credibility of a piece of information Cre(In) is evaluated through a measure of consensus (corroboration degree) between a set of belief functions, and this measure serves as a basis for quantifying the credibility of the source (sensor or fusion node) itself, Cre(Sk), used then as a discounting factor for all further belief functions provided by Sk. The process is dynamic in the sense that the credibility of the source is revisited in the light of new incoming piece of information. The method proposed relies on a hybrid fusion topology in which the sensors are grouped according to the feature they measure (similar and dissimilar sensors), allowing to select different kinds of measure for estimating the corroboration degrees. Through simulations, we compare (a) the hybrid-combination using the source credibility and the robust combination rule (RCR-L) accounting automatically for sensors's credibility; (b) the hybrid-combination, with different membership degrees and corroboration degrees used to estimate the sources credibility. We show that the new hybrid topology together with the credibility-based evidence discounting estimation algorithm provide a faster identification of the observed object.

Keywords

Dempster-Shafer theoryevidence theorydiscountingcredibilityfusion architecture
Type
Research Article
Information
RAIRO - Operations Research , Volume 44 , Issue 4: COGIS'09 , October 2010 , pp. 285 - 306
Copyright
© EDP Sciences, ROADEF, SMAI, 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

J. Besombes, V. Nimier and L. Cholvy, Information Evaluation in Fusion using Information Correlation, in Proc. of the 12th International Conference on Information Fusion, Seattle, USA (2009).
L. Cholvy and V. Nimier, Information Evaluation: discussion about STANAG 2022 recommandations, in Military Data and Information Fusion, RTO-MP-IST-040, Prague, Czech Republic (2003).
F. Cuzzolin, A Geometric Approach to the Theory of Evidence. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 38 (2008) 522–534.
Deng, Y., Shi, W., Zhu, Z. and Liu, Q., Combining belief functions based on distance of evidence. Decis. Support Syst. 38 (2004) 489–493.
J. Diaz, M. Rifqi and B. Bouchon-Meunier, A similarity measure between basic belief assignments, in Proc. of the 9th International Conference Information Fusion, Firenze, Italy (2006).
M.C. Florea, E. Bossé and A.-L. Jousselme, Metrics, Distances and Dissimilarity Measures within Dempster-Shafer Theory to Characterize Sources' Reliability, in Proc. of Cognitive Systems with Interactive Sensors (COGIS '09) (2009).
M.C. Florea and E. Bossé, Crisis Management Using Dempster Shafer Theory: Using Dissimilarity Measures to Characterize Sources' Reliability, in Proc. of C3I in Crisis, Emergency and Consequence Management, RTO-MP-IST-086, Bucharest, Romania (2009).
Florea, M.C., Jousselme, A.-L., Grenier, D. and Bossé, E., Robust combination rules for evidence theory. Inform. Fusion 10 (2009) 183–197. CrossRef
H. Guo, W. Shi and Y. Deng, Evaluating Sensor Reliability in Classification Problems Based on Evidence Theory. IEEE Transactions on Systems, Man, and Cybernetics. Part B: Cybernetics 36 (2006) 970–981.
A.-L. Jousselme and P. Maupin, On some properties of distances in evidence theory, in Proc. of the Workshop on the Theory of Belief Functions, Brest, France (2010).
Jousselme, A.-L., Grenier, D. and Bossé, E., A new distance between two bodies of evidence. Inform. Fusion 2 (2001) 91–101. CrossRef
Liu, W., Analyzing the degree of conflict among belief functions. Artif. Intell. 170 (2006) 909–924. CrossRef
R.C. Luo and M.G. Kay, Multisensor Integration and Fusion in Intelligent Systems. IEEE Transactions on Systems, Man, and Cybernetics 19 (1989) 901–931.
A. Martin, A.-L. Jousselme and C. Osswald, Conflict measure for the discounting operation on belief functions, in Proc. of the 11th Annual Conference on Information Fusion, Cologne, Germany (2008).
Mercier, D., Quost, B. and Denœux, T., Refined modeling of sensor reliability in the belief function framework using contextual discounting. Inform. Fusion 9 (2008) 246–258. CrossRef
V. Nimier, Information Evaluation: a formalisation of operational recommandations, in Proc. of the 7th International Conference on Information Fusion, Stockholm, Sweden (2004).
Ristic, B. and Smets, P., The TBM global distance measure for the association of uncertain combat ID declarations. Inform. Fusion 7 (2006) 276–284. CrossRef
Ristic, B. and Smets, P., Global cost of assignment in the TBM framework for association of uncertain ID reports. Aerosp. Sci. Technol. 11 (2007) 303–309 . CrossRef
G. Rogova and E. Bossé, Information quality in information fusion, in Proc. of the 13th International Conference on Information Fusion, Edinburgh, Scotland (2010).
G. Shafer, A Mathematical Theory of Evidence, Princeton University Press (1976).
Smets, P., Belief functions: The disjunctive rule of combination and the generalized Bayesian theorem. Int. J. Approxim. Reason. 9 (1993) 1–35. CrossRef
Tessem, B., Approximations for efficient computation in the theory of evidence. Artif. Intell. 61 (1993) 315–329. CrossRef
C. Wen, Y. Wang and X. Xu, Fuzzy Information Fusion Algorithm of Fault Diagnosis Based on Similarity Measure of Evidence, in Advances in Neural Networks, Ser. Lect. Notes Comp. Sci. 5264, Springer Berlin/Heidelberg (2008), pp. 506–515.
Xu, G., Tian, W., Qian, L. and Zhang, X., A novel conflict reassignment method based on grey relational analysis (GRA). Pattern Recogn. Lett. 28 (2007) 2080–2087. CrossRef
Yager, R.R., On considerations of credibility of evidence. Int. J. Approxim. Reason. 7 (1992) 45–72. CrossRef
S. Young and J. Palmer, Pedigree and confidence: Issues in data credibility and reliability, in Proc. of the 10th International Conference on Information Fusion, Quebec city (Qc), Canada (2007).