An Application of DSmT in Ontology-Based Fusion Systems
KsaweryKrenc Adam KawalecAbstractTheaimofthispaperistoproposean
ontologyframeworkforpreselectedsensorsduetothe sensor networks
needs, regarding a specific task, such as
thetargetsthreatrecognition.Theproblemwillbe solved
methodologically, taking into account particularly
non-deterministicnatureoffunctionsassigningthe concept and the
relation sets into the concept and relation
lexiconsetsrespectivelyandvice-versa.Thismay
effectivelyenhancetheefficiencyoftheinformation fusion performed in
sensor networks. Keywords:Attributeinformationfusion,DSmT,belief
function, ontologies, sensor networks. 1Introduction
Ontologiesofthemostappliedsensorsdonottakeinto
accountneedsofsensornetworks[1].Sensors,in particular the more
complex ones, like radars or sonars are intended to be utilized
autonomously. Thefoundationofthesensornetworks(SN), comprehended as
the networks of cooperative monitoring,
isunderstandinginformationobtainedfromsome
elementsbyanotherones.Thusthequestionofthe
commonlanguageisveryimportant.Theontologyof sensor network should
be unified and structured.Thekeyprobleminthispaperisneitheradirect
application of existing solutions in the field of ontologies
forthesensornetworksnoradesignofanewontology,
readytoimplement.Theaimistoproposetheontology
frameworkfornetworks,consistingofpreselected
sensors,duetothesensoryneeds,toperformaspecific task, such as
recognizing the target threat. Theselectionofthesensorswillbetaken
in four particular steps, namely: 1. Describing, what particular
pieces of information arerequired to define the target threat; 2.
Describing,whatparticularsensorsenabletogainthe mentioned pieces of
information; 3. Identificationofallinformationpossibletoacquireby
preselected sensors; 4. The specific sensor selection;2Sensor type
selection Thissectionfocusesoncreatingtheontologyofasensor
network,processinginformationrelatedtothetarget threat attribute.
Mentioned information may be classified, according to its origin,
as: Observableoriginateddirectlyfrom sensors orvisual sightings;
Deductable (abductable) designated by the
wayofdeductivereasoning,basedontheotherobservable attributes,
gathered previously; Observable and deductable designated both:
onthebasisofobservationandbythewayofdeductive reasoning; Confirmed
verified by other information centeror external sensor
network;Theobservableattributesmaybedefinedbasedon
informationoriginatedfromdiversesensors.Forthe
purposeofthispaperthescopeofsensors(possibleto
utilize)willbeconstrainedtotheset,whichinthe
authorsopinionfullyreflectstherequiredinformation about the target
in the real world.It is a very important assumption that the
selection of sensortypesisconditionedontologically.Thatmeans
neitheranyparticularsensormodelnorcommunication
protocolnoranyotherelementoftheSNorganizations will be discussed.
From the observers point of view (whose main duty
istoassessthetargetthreat)itisimportanttodefinethe following
features of the target:
Keyattributeofthetarget:thethreat(basedonobservations);
Additionaltargetattributes(asthebasisfordeductionreasoningaboutthethreat)i.e.theplatform,(frigate,corvette,destroyer)andtheactivity(attack,reconnaissance,search&rescue);
Auxiliary characteristics: target position;Originally published as
Krenc K., Kawalec A., An application of DSmT in ontology-based
fusion systems, in Proc. of Fusion 2009, Seattle, WA, USA, 6-9 July
2009, and reprinted with permission.Advances and Applications of
DSmT for Information Fusion. Collected Works. Volume 42792.1Types
of sensors Preselectedtargetfeaturesmayberegisteredbyvarious means
of observation, namely:
Position:Radar(allspatialdimensions),sonar,IRsensor(mostlytodefinetargetazimuthandelevation);
Threat:IFF,visualsightings(human),videocamera (daylight or
noctovision); Platform:visualsightings,videocamera,thermo-vision
camera; Activity: visual
sightings.Theabovestatementmayberegardedasapre-selectionofsensorset,usedinthefollowing
considerations of this paper. It is important to notice, that
someofthementionedsensorsmayacquireinformation
relatedtomorethanoneattribute.Therefore,areversed
assignment(sensorstoattributes)seemstobemore
adequate.2.2Sensor-originated information
Figure1presentsthepreselectedtarget features and their
inclusionrelations.Additionally,itwaspointedoutthe
examplesensors,whichenabletoacquirethementioned information.
PositionRadarIFFVideocameraThreatPlatformActivityVisualsightingsFigure
1 Information scope originated from diverse types of sensors.
Itshouldbenotedthatalthoughsomeofthese sources allow for obtaining
information on more than one attribute, it is possible to identify
a hierarchy of relevance ofthisinformation.Thatmeansthatsomeofthe
attributes,however,possibletorevealfrommultiple
sources,forsomesourcesperformtheprimary information while for
others the secondary information: Radar: position1; IFF: position,
threat; Video camera: position, platform, threat;
Visualsightings:position,threat,platform,activity;For visual
sightings, where the human plays the role
ofthesensor,itisdifficulttoidentifytheprimary
information.Amongtheabovesourcesthevisual recognition is the most
reliable way of defining the target
activity.Therefore,takingintoaccountthefactthatit allows to
identify the target threat and platform, the visual
recognitionmaybeconsideredasaspecificsourceof
information.Theseobservationsarehighlyimportantforfuture
considerations,whichwillbeeffectivelyusedincreation
ofthehierarchyoftheconceptlexiconsaswellasin defining the relations
among concepts of SN ontology. Someofthesesensorsperformverycomplex
devicesandrequiretheintroductionofcertaininterfaces,
allowingtheautomaticacquisitionofusefulinformation
(intermsofsensornetworks).Anexampleofsucha sensor is a video
camera. In order to make effective use of
animagefromthevideocameraaspecificmoduleis
necessarytointerpretthetakenpicture,identifyingthe
significantfeaturesoftheobjectofinterest.Inthatcase,
theontology,thevideocameraisdefinedinthatvery
moduleanditismodifiableaslongasthereisaccessto
theconfigurationofthatmodule.Thisleadstoanother possible
classification of sensors: Constant (invariant) ontology sensors,
e.g. IFF;
Variantontologysensors,e.g.videocameraequippedwithinterpretationmoduleorvisualsightings;Guidedbytheprincipleofmaximuminformation
growth,innextstagesofcreatingtheSNontologythe
followingsourcesofattributeinformationwillbetaken into account:
IFF, video camera (VC) and visual sightings (VS). 3Defining sets of
SN ontologies Referringtoataxonomyofthetermofontology[1]the
authorswouldliketonoticethattheproblemofSN ontology concerns, in
particular, the so-called method and task ontologies.
Therehavebeeneffectivelyutilizedconcept
lexiconsofJointC3InformationExchangeDataModel 1 Underline means the
prime information. Advances and Applications of DSmT for
Information Fusion. Collected Works. Volume
4280[2],constrainingtheconsiderationstothreeoftheJC3 model
attributes: threat: object-item-hostility-status-code; platform:
surface-vessel-type-category-code; activity:
action-task-activity-code;While defining the attribute relation
functions, the Dezert-SmarandacheTheory(DSmT)ofplausibleand
paradoxical reasoninghas been utilized [3]. 3.1Rules for sensor
network ontologies selection In section 2.2 there was proposed a
sensor distinction for
variantandinvariantontologysensors.Consideringthis
divisionisfundamentalwhilecreatingSNontology, which takes place in
four stages: 1.
Creatingthefundamentalconceptlexiconforasensornetwork,basedoninvariantconceptlexicons
of particular sensors;2. Creating the auxiliary concept lexicon for
sensornetwork,basedonvariantconceptlexiconsofparticular sensors;3.
Extendingthefundamentalconceptlexiconwiththe auxiliary lexicon;4.
Definingrelationsamongtheconceptsinsensornetwork;According to the
definition of ontology, given in [4], [5], SN ontology may be
formulated as follows:R C G F G F L O , , , , , , = (1) where: L is
either concept or relation lexicon;
Flexiconelementstoconceptsassigning function;
Glexiconelementstorelations assigning function;F
afunctionreversedtoF, assigningconceptstoelementsoftheconcept
lexicon;G afunction reversed to G, assigning relations to elements
of the relation lexicon;Casetof the whole concepts used in SN;Raset
of the whole relations used in SN.
AccordingtothelexiconsofJC3model,theabove
mentionedconceptsandfunctionswillbedefinedinthe following
subsections. 3.1.1Concepts Concepts are representations of a
certain group of objects
ofthesamecharacteristics,whichmaybedirectly
identifiedbyselectedsubsetofelementsoftheconcept
lexicon[5].Thatmeans,thatassigningforexamplean attribute hostile
target to a target uses the concept of the
hostiletarget,whichistheelementoftheset(C)ofall possible concepts
for a given sensor network.
Anotherquestionisarepresentationoftheconcept
hostiletargetinthelanguageoftheparticularsource. For instance: for
IFF device it will be the value of FOE,
andforavideocamerathevalue,definedinthe interpreting module as
HOSTILE. Mathematically,theFassignmentisnotabijection in general,
moreover: it is not a function. In case multiple
sourcesareutilized,theFisnotaninjection,whereasif the concept set
is rich, comparably to the poor lexicon
theFisnotinjective.ThismayoccuriftheSN,prepared
fordefiningfullytargetthreat,isusedfordeciding whether the target
is either friend or hostile. Then, the F
willinterpretconceptsoftraininghostile,training
suspectandassumedfriendasfriendassigningthe lexical value ofFRIEND
[6]. InordertoillustrateFand Fassignmentitis suggested to consider
the following example.Example 1:Letthesetofconceptsbedefinedas
follows:C = {friend, assumed friend ,assumed hostile, hostile} (2)
and the concept lexicon is defined as follows: Lc = {FRIEND,
HOSTILE, ASSUMED}(3) Thus,itispossibletodefinesubsetsoftheconcept
lexiconelementsinsuchawaythattheFassignment would be a bijection
(Figure 2).FRIENDASSUMED FRIENDASSUMED HOSTILEHOSTILEfriendly
targetassumed friendlytargetassumed enemytargetenemy
targetFFFFFFFFFigure 2 F-assignment as a bijection. Advances and
Applications of DSmT for Information Fusion. Collected Works.
Volume 4281Definingsubsetsoflexicalelementsassingletons leads to
non-function F assignment (Figure 3). ASSUMEDfriendly targetassumed
friendlytargetassumed enemytargetenemy targetFRIEND HOSTILEFFigure
3 F as a non-function assignment.
Incaseofrichconceptlexiconsetsitisimportant to express subsequent
target types as conjunctions of their distinctive features. Example
2: Table 1 Example definitions of surface platforms Transporter AUX
AIR D TRAN Command AUXS&MCALAIR C2 where: AUX auxiliary vessel;
S&MCALequippedwithartilleryofsmalland medium caliber; AIR
against the air targets; D performs landing operations; C2 command
& control; TRAN transport of landing forces; 3.1.2Relations
Relationsdefinetherelationshipsamongconcepts.
Relationmaybehierarchicalorstructural.Moreover,for the purpose of
sensor networks, they may be classified as: Relations I, among the
observable attributes ofadiverse type;
RelationsII,amongattributesofmiscellaneousorigin;
RelationsIII,amongtheidenticalattributes,originated from diverse
sources;Relationsamongtheobservableattributesofa
diversetypeenableadeductionofsomeattributes values based on
observable values of another ones. For instance:
therelationsbetweenthethreatandtheplatformofthe
targetenablethedeductionoftargetactivity.Linkingthe
subsequentobservableattributesisperformedaccording to mentioned in
previous section distinctive features of the
target.Thismeansthatforexample:defining(basedon
observations)thetargetplatformisequaltoassigningto
thetargetsomeofdistinctivefeatures,whichthetarget, performing the
particular activity, has to possess.
Relationsamongattributesofmiscellaneousorigin:
observableanddeductableresultinso-calledobservable-deductableattribute.Theeffectiveinformationfusion
from multiple sources is performed according to the rules
ofcombinationandconditioning,obtainedfromDSmT [7], [8]. This
process is going to be described in details in section 3.2.
Relationsamongtheidenticalattributes,originated
fromdiversesourcesarethetypeofrelations,wherethe
keyquestionisalexicalvarietyofconceptsusedby
particularsources.Forinstance:thethreatattributevalue
acquiredfromIFFmaybeeitherFRIENDorFOE, whereas the same attribute
obtained from visual sightings
maybeof{FRIEND,HOSTILE,UNKNOWN,JOKER,
FAKER,}.InsuchacaseavalueofFRIEND,gained
fromIFF,correspondstotheexactvalueofthevisual
sightings.ThevalueofFOEisequaltoHOSTILE,
whereastherelationsamongvaluesofFRIEND,gained
fromIFFandFAKER(orJOKER),gainedfromthe
visualsightingsarenotsoobviousandtheymustbe
defined,accordingtothedefinitionsofthesetraining types (JOKER,
FAKER).3.2Proposition of sensor network ontology
Thissectionpresentsapropositionofanontology framework for a sensor
network, dedicated to monitor the
targetthreat.Inthesolutiontherewereutilizedconcepts
andconceptlexiconsofJC3model.Theauthors intention was to show the
way relations of three attributes
(threat,platformandactivity)shouldbedefined,rather than to present
the complete SN ontology.
Table2presentsabijectiveassignmentofconcepts
toelementsofaconceptlexicon.Asitwasmentioned before, this
assignment need not be a bijection, however it
isdesirableespeciallyifsetsofvaluesforattributesof platform and
activity are numerous. Table 2 SN ontology: concepts and concept
lexicon. Concepts Concept lexiconAnOBJECT-ITEMthat isassumedtobea
friend becauseofits characteristics,behavior or origin. ASSUMED
FRIEND Threat AnOBJECT-ITEMthat
object-item-hostility-status-codeHOSTILE Advances and Applications
of DSmT for Information Fusion. Collected Works. Volume
4282ispositivelyidentifiedas enemy. according to JC3 according
toJC3 Generaldesignatorfor aircraft/multi-role aircraft carrier;
AIRCRAFT CARRIER, GENERAL Craft40metersorless employedtotransport
sick/woundedand/or medical personnel. AMBULANCE BOAT Platform
according to JC3surface-vessel-type-category-code according toJC3
Toflyoveranarea, monitorand,where necessary, destroy hostile
aircraft,aswellas protectfriendlyshipping inthevicinityofthe
objective area. PATROL, MARITIME Emplacementor deploymentofoneor
more mines. MINE-LAYING Activity according to
JC3action-task-activity-code according toJC3
Theassignmentofrelationsamongattributesto
relationlexicons(Table3)isasurjection.Inorderto
definetherelationsamongattributesDSmTcombining
andconditioningruleshavebeenapplied.Thepreferred
ruleforconditioningistheruleno.12.Whencombining
evidence,thereisapossibilitytousemanycombination
rules,dependingtheparticularrelation.However,for
simplicity,itissuggestedtoapplytheclassicruleof
combination(DSmC),whichhaspropertiesof commutativity and
associativity. Table 3 SN ontology: relations and relation lexicon.
Relations Remarks Relationlexicon cond(.)Based on
DSmTConditioningRel. I: According to distinctive features
Implication cond(.)Based on DSmTConditioningRel. II: Based on
DSmTCombination cond(.)Based on DSmTConditioningRel. III:
BasedonDSmT (combinationrule neednotbeidentical withoneinRelations
II) Combination Below,therehavebeenpresentedexamplesof particular
types of relations. In case of the relation of type
Iitispossibletoreasonaboutavalueofacertain
attribute,basedontheknowledgeabouttheotherones.
However,iftheunambiguousdeductionofthethird
attributeisnotpossible,duetothemajorityofpossible solutions, an
application of abductive reasoning (selection of the optimal
variant) seems to be justified.Relations I:
(Threat,Platform)Activity:(FAKER,FRIGATE TRAINING) TRAIN
OPERATIONS; (Threat,Activity)Platform:(FAKER,TRAIN OPERATIONS)
TRAINING CRAFT; (Platform,Activity)Threat:(HOUSEBOAT, PROVIDE
CAMPS) NEUTRAL; Relations II: FAKER=cond(obs(FAKER) ded(FAKER)
obs(FRIEND)); Relations III: FAKER=cond(obs(FAKER) VS(FAKER)
IFF(FRIEND)); Theabductivereasoningprocessmaybesystemizedby
applicationofDSmT,wheretheselectionoftheoptimal
valuetakesplaceaftercalculatingthebasicbelief assignment. Example
3: (Threat,Activity)Platform:(FRIEND,MINE HUNTING MARITIME)
MINEHUNTER COASTAL (MHC) MINEHUNTERCOASTALWITHDRONE(MHCD)
MINEHUNTER GENERAL (MH) MINEHUNTER INSHORE (MHI)
MINEHUNTEROCEAN(MHO) MINEHUNTER/SWEEPER COASTAL (MHSC)
MINEHUNTER/SWEEPER GENERAL (MHS) MINEHUNTER/SWEEPEROCEAN(MHSO)
MINEHUNTER/SWEEPER W/DRONE (MHSD) Applying DSmT, for each of
possible hypothesis a certain mass of belief is assigned, e.g.:
m(MHC) = 0.2, m(MHCD) = 0.3,m(MH)=0.1,m(MHI) = 0.1,m(MHO) = 0.1,
m(MHSC)=0.05,m(MHS) = 0.05, m(MHSO) = 0.05,m(MHSD)= 0.05 Based on
the obtained basic belief assignment (bba) belief
functions,referringtoparticularhypotheses,maybe
calculated.Inthesimplestcase,assumingallofthe
hypothesesareexclusive,thesubsequentbelieffunctions
willbeequaltorespectivemasses,e.g.Bel(MHC)= m(MHC), Bel(MHCD) =
m(MHCD), etc. Morecomplexcase,whererelationshipsamong
hypothesesaretakenintoaccountwillbeconsideredin the next section.
Advances and Applications of DSmT for Information Fusion. Collected
Works. Volume 42834Verificationoftheusefulnessof elaborated
ontology sets ThepresentedframeworkoftheSNontology,forthe
purposeofthetargetthreatassessment,requiresa
verification.Particularly,itisimportanttoverifythe
correctnessofreasoningprocessesandacombinationof the reasoning
results with observation
information.Theproposedsolutionsubstantiallydiffersfromthe
existingdeterministicontology-basedmethodsbecauseit
introducesexplicitlytheuncertaintyoftherelations
amongtargetattributes.Therefore this section was meant
tofocusontheverificationoftheserelationreasoning
mechanismsratherthanthecompletenessofthetarget representation by
the sensor network.4.1Assumptions In order to verify the usefulness
of the proposed ontology framework, a specially designed
demonstrator application
forevaluationofthetargetthreatinformationhasbeen used. This
application enables a simulation of acquiring of
informationfromdiversesources,like:radar,video camera and visual
sightings.It is assumed that the visual sighting is also a source
ofinformationaboutatargetplatformandatarget
activity.Thebbavaluesforplatformandactivity
attributeshavebeenassignedarbitrary.During
experimentationtheobservableattributesaswellas
deductableattributeshavebeentakenintoaccount. Frames of discernment
for observation and deduction may
differingeneral.Forthepurposeofverificationof
proposedontologysets,anexamplefromthesection3.2 is to be
considered. Additionally it is assumed: Application of the hybrid
DSmT model:o The hypotheses are not exclusive;o
ThehypothesescorrespondtotheJC3model terminology;
InrelationsoftypeIIandIIIthehybridruleofcombination (DSmH) has been
applied; Theconditioningruleno.12hasbeenusedforupdating
evidences;4.2Numerical experiments
Figure4showsarandomlygeneratedtrajectoryofthe
targetofwhichthethreatvalueisatstake.Observations
aretakenfromthreesources(visualsightings,radar system - IFF and
video camera) synchronously.Thegreencolormeanssuccessivelyacquired
observations for each of the sources. The red color means
theobservationsimpossibletoacquirebecausethetarget
wasoutsideofthedetectionzoneforaparticularsource
[3].Takingforexamplethelastsample,therespective bba are as Table 4
shows. Figure 4 Randomly generated target trajectory and its threat
evaluation based on radar, VS and VC observations.
Table4Bbagatheredfromdiversesources:visual sightings, video camera
and radar. Threat Visual Sightings VideoCamera Radar/IFFHOS
0.00240.00040.0008 UNK 0.00600.0012- NEU 0.00680.0015- JOK 0.0109 -
- FRD 0.24000.43680.8773 FAK 0.02920.00490.0119 SUS
0.00320.00050.0011 AFR 0.02150.00460.0088 PEN 0.68000.55000.1000 A
relation of type III of combining information from IFF and the
visual sightings results in acceptance the target is friendly:
FRIEND Threat ThreatIFF VS (4) From the visual sightings it is also
acquired that the target
activityismine-hunting(MINEHUNTINGMARITIME).
Thus,therelationoftypeI,betweenthethreatandthe activity attribute
results in selection of the target platform, related to searching
for mines. (FRIEND, MINE HUNTING MARITIME) platform(5) Advances and
Applications of DSmT for Information Fusion. Collected Works.
Volume 4284Intheconsideredcaseitisassumedtheframeof
discernmentoftheplatformattributeoriginatedfromthe video camera is
defined as follows: VC = {MHC, MHI, MHO, MSC, MSO, D}(6) where: MHC
MINEHUNTER COASTAL;MHI MINEHUNTER INSHORE; MHO MINEHUNTER OCEAN;
MSC SWEEPER COASTAL;MSO SWEEPER OCEAN;D DRONE; Additionally,with
and operatorsthesecondaryhypotheses may be created, which refer to
another values oftheplatformattribute(surface-vessel-type-category
code) of JC3 model: MHCD=MHCD(MINEHUNTERCOASTALWITH DRONE);
MHIMHOMHCD=MH(MINEHUNTERGENERAL);
MHOMSO=MHSO(MINEHUNTER/SWEEPEROCEAN); (MHCMSC)D= MHSD
(MINEHUNTER/SWEEPER
W/DRONE);(MHOMSO)(MHCMSC)D=MHS(MINEHUNTER/SWEEPER GENERAL);
Thebasicbeliefassignmentforthevideocamera observation may be
defined as follows: mVC(MHC) = 0.1, mVC (MHCD) = 0.1, mVC (MSC) =
0.2, mVC (MHI) = 0.3,mVC (MHO) = 0.2,mVC (MSO) = 0.1,Due to the
implication (5) the above bba may be modified according to BCR12
with a following condition: MHI MHO MHC Truth Cond = : (7) Figure 5
Venn's diagram for the platform attribute. The truth is grey
colored. Thus,theresultingbbafortheplatformattributeis updated, as
follows:
mR(MHC|Cond)=mVC(MHC)+mVC(MHCD)=0.2,mR(MHSC|Cond)=mVC(MSC)=0.2,
mR(MHI|Cond)=mVC(MHI)=0.3,mR(MHO|Cond)=mVC(MHO)=0.2,
mR(MHSO|Cond)=mVC(MSO)=0.1,
which,aftercalculatingtherespectivebeliefand
plausibilityfunctions,leadstoacceptationofthe
hypothesisofMHC(MINEHUNTERCOASTAL)forthe platform attribute of the
whole sensor network. It is worth of notice that the belief
function for MHC before updating is of the least value since: BelVC
(MHC) = mVC(MHC)= 0.1(8)
Afterupdating,duetothefactthatmVC(MHSC)supports
thebeliefinMHChypothesis,thishypothesisbecomes the most credible
since: BelR (MHC) = mR(MHC)+ mR(MHSC)= 0.4(9) 5Conclusions The
results of the numerical experiments, presented in the
previoussection,haveproventhattheapplicationof
DSmTforthepurposeofdefiningrelationsamongtarget
attributes,givesthepossibilityofunificationof
informationacquiredfromsensorsaswellasobtained
basedonthedeductivereasoning.Thatinfluences effectively the wholeSN
ontology, due to the fact the SN
conceptlexiconbecomessubstantiallymodified.Itdoes
notprovideaunionoflexiconsforeachsensor,which
wouldbeexpectableinthedeterministiccase.TheSN
conceptlexiconbecomesextendedwithintersectionsand
unionsofthehypothesescreateduponthelexiconsof particular sensors.
DuringtheexperimentsithasbeenutilizedtheJC3
modelslexiconofsurface-vessel-type-category-code
attribute.Itisimportanttonotice,thatdespiteitslarge
volume,thelexiconisnotstructured.Thus,anemerging conclusion occurs,
that setting JC3 lexicons in a hierarchy
wouldbringtangiblebenefitsduetothefactthatthe
hierarchyenablescreatingthehypothesesusing
andoperatorsmoreeffectively,andthisinturnincreasestheprecisionofthereasoningprocessesbasedon
information acquired from sensors. Acknowledgements The research
work described in this document is a part of
extensiveworksdevotedtosensornetworksinNEC
environment.Itwasfinancedwithsciencemeansfrom 2007 to 2010 as an
ordered research project. Advances and Applications of DSmT for
Information Fusion. Collected Works. Volume 4285References [1]K.
Krenc: An introductory analysis of the usefulness
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83-920120-5-4, 2008. [2]TheJointC3InformationExchangeDataModel,
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