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TOPIC 8: LEVEL 1 IDENTIFICATION
David L. Hall
TOPIC OBJECTIVES
Continue introduction of Level-1 processing with focus on attribute fusion (e.g., for target identification)
Introduce common pattern recognition algorithms
Understand issues and limitations
LEVEL-1 (IDENTITY DECLARATION)
JDL LEVEL ONE PROCESSINGOBJECT REFINEMENT
JDL Level One Processing Object Refinement
Data Alignment
•Spatial Reference Adjustment
•Temporal Reference Adjustment
•Units Adjustment
Data/Object Correlation
Object
Positional Estimation
• System Models• Optimization Criteria• Optimization Approach• Processing Approach
Object Identity
Estimation
• Physical Models• Feature-based Inference Techniques
• Cognitive-based Models
CA
TEG
OR
YFU
NC
TIO
NP
RO
CES
S
• Gating• Association Measures• Assignment Strategies
Sources HumanComputerInteraction
DATA FUSION DOMAIN
Level OSignal
Refinement
Level OneObject
Refinement
Level TwoSituation
Refinement
Level ThreeThreat
Refinement
Level FourProcess
Refinement
Database Management System
SupportDatabase
FusionDatabase
CONCEPTUAL PROCESSING FLOW FOR LEVEL 1 FUSION
BulkGating
DataAssociation
Position/Kinematic/Attribute
Estimation
IdentityEstimation
• Observation File• Track File• Sensor Information
Sensor#1
PreprocessingData
Alignment
Sensor#2
PreprocessingData
Alignment
SensorN
PreprocessingData
Alignment
•
•
•
•
•
•
•
•
•
METHODS FOR ATTRIBUTE FUSION
Identity or classof entity, object or activity •
• •
Feature-basedClassificationMethods• Neural nets• Cluster algorithms• Parametric templates
Decision-based methods• Voting• Decision trees• Logical templates• Bayesian Belief nets• Dempster-Shafer method• Fuzzy logic• Rule-based
Systems
Fea
ture
ex
tra
ctio
n
S1
S2
SN
Model-based methods(high-fidelity physical models)
Raw Data FeaturesDeclarationof Identity
Declaration of Identity
EXAMPLE OF SINGLE-SENSOR FEATURE BASED OBJECT IDENTITY
DECLARATION
Pro
pag
atio
n M
edia
Sensors
Signal Space
Fea
ture
Ext
ract
ion
y
Target Models of a priori data
Classifier
• Cluster Methods• Neutral Networks• Templating• etc.
Feature Space
Target Class A
Target Class B
DecisionSpace
EnergySensorReaction
Signal,Image
Feature Vector
Declaration Of Identity
CLUSTER ANALYSIS
Cluster Analysis:Cluster Analysis: Basic use is for classification analysis based on multi-parameter
similarity. Provides estimation of pair-wise/cluster-wise similarities. Supports parametric model development. Helpful when studying new entities/new parameters. Can be computationally demanding.
CONCEPT OF CLUSTER ANALYSIS
Sensor A
Sensor B
Sensor C
• • •
TaggedDataSet:
ObservationsAssociated
withSpecificObjects
Selectionand
Calculationof
ResemblanceCoefficients
Selectionand
Calculationof
ClusteringMethod
ClusteringThresholdSelection
ClusterDefinition
Object
Ob
jec
t
ResemblanceCoefficients
Observation/Featurei
Observation/Featurej
Cluster i
Cluster j y
EXAMPLE: HELICOPTER TRANSMISSION FAULT
CLASSIFICATION
Purpose: Classify faults using data collected from the
aft transmission of a Westland CH-46E helicopter 8 accelerometers 7 faults and a no-fault Faulty and seeded fault components
used
Results: Achieved robust classification using feature
reduction Separates faults with similar signatures but
with significantly different criticalities failure progressions
Sp
iral
Be
vel
Inp
ut
Pin
ion
Sp
alli
ng
Input PinionBearing Corrosion
Quill ShaftCrack
CollectorGearCrack Helical
Input PinionChipping
HelicalIdler Gear
Crack
NoDefect
A. K. Garga et al, ”Fault Classification in Helicopter Signals,“ Proc. Amer. Helicopter Soc. 53rd Annual Forum, 1997.
CLUSTER ALGORITHMIC APPROACHES
Hierarchical agglomerative methods Iterative partitioning methods Hierarchical divisive methods Density search methods Factor analytic methods (based on correlation
matrix processing) Clumping methods (allows membership in more
than one class) Graph theoretic methods
ASSESSMENT OF CLUSTER METHODS
The good news Requires no a priori “knowledge” of data or
target physical characteristics Allows exploration of features and classes Simple to use Extensive COTS software available, see the
review at site http://www.pitt.edu/~csna/software.html
The bad news Requires extensive training data Results dependent upon
Association measure Scaling of feature components Order of data processed Clustering scheme Specific training data etc
OVERVIEW OF ADAPTIVE NEURAL SYSTEMS
InputVector
OutputVector
w0
w2
w1
wn
x0
x2
x1
xn
f(y)Output
Adaptive linear combiner
y
THE ACTIVATION FUNCTION
*V.R. Hush and B.G. Hornefs(y) = (1 - ey)-1
1.0
0.0
-10.0 0.0 10.0
f(y)
y(a)
1.0
0.0
-10.0 0.0 10.0
f(y)
y(b)
=5.0
=1.0
=0.2
EXAMPLE: HANDWRITING RECOGNITION
F
eatu
re V
ecto
r
1 = a
0 = b
0 = c• • • • • •
0 = z
NEURAL NETWORK ISSUES AND LIMITATIONS
• Choosing the Network SizeChoosing the Network Size- Tradeoff between too large and too small- Emerging systematic techniques for size selection
• Complexity of LearningComplexity of Learning- Back-propagation (BP) Methods notoriously slow- Weighting search problem is NP-complete
• GeneralizationGeneralization- How much training data required for general results?
(rule of thumb is 10 x wij)
- Generalization error (error on training data vs actual problem)
• Network InterconnectivityNetwork Interconnectivity- Optimal brain damage- Complexity regularization- Weight sharing
PARAMETRIC TEMPLATING AND DECISION TREES
If we know a priori the parametric “boundaries” related to decision or identification classes then we may represent these via parametric templates, decision-trees, or rule-based systems; e.g.
Mechanical system fault if Engine temperature exceeds Tcritical
Possible bearing failure if vibration exceeds X, Etc.
COMBINED SYNTACTIC/CONTEXTUAL TARGET MODELING
• Found at specified altitudes• Minimum speed equals 150mph• Travels in specified groups
Model consists of target signature and contextual informationModel consists of target signature and contextual information
Stored Models
Weather (MET)
Time/Season
Range
Sensor Phenomenology
TargetSignature
ContextualModel
Design Target Model
PATTERN RECOGNITION
SYNTACTICAL COMPOSITION
CONTEXTUAL INTERPRETATION
ALTERNATE ARCHITECTURES FOR MULTISENSOR IDENTITY FUSION
ASSOCIATION
DecisionLevel
Fusion
IdentityDeclaration
JointIdentity
Declaration
IdentityDeclaration
IdentityDeclaration
IdentityDeclaration
I/DA
I/DB
I/DN
• • •
FEATURE
EXTRACTION
SensorA
SensorB
SensorN
• • •
A. Decision-Level FusionA. Decision-Level Fusion
ALTERNATE ARCHITECTURES FOR MULTISENSOR IDENTITY FUSION
B. Feature-Level FusionB. Feature-Level Fusion
ASSOCIATION
FeatureLevel
Fusion
IdentityDeclaration
JointIdentity
Declaration
FEATURE
EXTRACTION
SensorA
SensorB
SensorN
• • •
ALTERNATE ARCHITECTURES FOR MULTISENSOR IDENTITY FUSION
C. Data-Level FusionC. Data-Level Fusion
IdentityDeclaration
JointIdentity
Declaration
FEATURE
EXTRACTION
ASSOCIATION
SensorA
SensorB
SensorN
• • •
DataLevel
Fusion
FUZZY MATHEMATICS
The world of human cognition is not binaryThe world of human cognition is not binary Many concepts are not defined with math precision:Many concepts are not defined with math precision:
Examples of fuzzy notions about two • somewhat heavy ugly • handsome tall • borderline
Interpretation is context dependent Fuzzy set theory argues that imprecision is an intrinsic property of various Fuzzy set theory argues that imprecision is an intrinsic property of various
notionsnotionsNot an approximation of truthNot a failure to comprehendAn admission that some notions may forever be imprecise
Do not try to quantify the unquantifiable; but formalize a way to deal with itDo not try to quantify the unquantifiable; but formalize a way to deal with it
FUZZY SETS: MATHEMATICS
Introduce the membership function:Introduce the membership function:
A(X) [0, 1] V x E
Fuzzy sets are sets of ordered pairs:Fuzzy sets are sets of ordered pairs:
(x, (x)) Note:Note:
BOOLEAN FUZZY SETs
(x) [x, (x)]
T or F partial truth/uncertainty feasible
Membership functions are not unique:Membership functions are not unique:
Varying solutions
Sensitivity analysis to choice of membership function
FUZZY SETS:ELEMENTARY OPERATIONS
INCLUSION:INCLUSION: A B A (X) ≤ B (X) EQUALITY:EQUALITY: A = B A (X) = B (X) COMPLEMENTATION:COMPLEMENTATION: A (X) = 1 - A (X) UNION:UNION: (X)AB = MAX [A (X), B (X)] INTERSECTION:INTERSECTION: (X)AB = MIN [A (X), B (X)] DIFFERENCE: DIFFERENCE: (X)AB = MAX [A (X), B (X)]
FOR EXAMPLE, LETFOR EXAMPLE, LETA = (X10.2, X2 0.7, X3 1, X4 0.1)
ANDANDB = (X10.5, X2 0.3, X3 1, X4 0.0)
THENTHEN AB = (X10.5, X2 0.7, X3 1, X4 0.1)
ANDAND AB = (X10.2, X2 0.3, X3 1, X4 0.0)
INTERPRETING SOME FUZZY SET OPERATIONS: INTERSECTION
By definition, an element in a fuzzy set can reside partly in one set By definition, an element in a fuzzy set can reside partly in one set and partly in another (including the complementary set)and partly in another (including the complementary set) An element cannot be more true in the intersection than it is in either set An element cannot be in the intersection to a degree more than it is in one of
the subsets; this argues for min () Intersection creates a middle level type set
EXAMPLE: the intersection of TALL and NOT TALL setsEXAMPLE: the intersection of TALL and NOT TALL sets
DATA FUSION WITH FUZZY LOGIC
SENSOR1
y1
SENSORN
yN
FU
ZZ
IFIC
AT
ION
A
B
FUZZY LOGIC
• If A and B C• If A and B D
C
D
D
EF
UZ
ZIF
ICA
TIO
N
QuantifiedInferences
FuzzyMembershipFunctionXforms
Fuzzy RulesFuzzy Calculus
InverseFuzzyMembershipXforms
• Ad Hoc• Neural Nets• Templates• Etc.
APPLICABILITY OF TECHNIQUES FOR LEVEL 1 FUSION
Spatial Adj.
Temp. Adj.
Units Adj.
Screen-ing
Correla-tion
Assign-ment
Obs. Predict
State Update
Uncer-tainty Mgmt.
Object Mgmt.
ID Mea-sures
Com-pari-son
Declare
ID
Uncer-tainty Mgmt.
Algorithms and Techniques
Data
Alignment Data/Object Correlation
Object positional/ Kinematic/Attribute Estimation
Object Identity Estimation
Coordinate Transforms X X X X X Sensor Models X Physical Models X X X X X Association Measures X X X X X Assignment Logic X X X Equations of Motion X Optimization Methods X X
Kalman Filters X X Covariance Error X X Bayesian Inference X X X X Dempster-Shafer X X X X Voting X X Pattern Recognition X X X X Templating X X X X X Expert Systems X X X X Fuzzy Sets X X X
TOPIC 8 ASSIGNMENTS
Preview the on-line topic 8 materials Read chapter 5 of Hall and McMullen (2004) Writing assignment 7: Develop a one-page discussion of how
level-1 identification and pattern recognition applies to your selected application.
Discussion 4: Discuss the concept of identification; how have automated identification processes and sensors (e.g., tags on objects, cell phones, smart cards, etc) become integrated into common activities? What are issues of failure in automated identification techniques?
DATA FUSION TIP OF THE WEEK
Here is an ancient Chinese classification of animals:"Animals are divided into (a) those that belong to the Emperor, (b) embalmed ones, (c) those that are trained, (d) suckling pigs, (e) mermaids, (f) fabulous ones, (g) stray dogs, (h) those that are included in this classification, (i) those that tremble as if they were mad, (j) innumerable ones, (k) those drawn with a very fine camel's hair brush, (l) others, (m) those that have just broken a flower vase, and (n) those that resemble flies from a distance." from Other Inquisitions: 1937-1952 by Jorge Luis Borges Downloaded from http://www.alaska.net/~royce/Funny/classify.html July 30, 2008
It is easy to forget that identification and classification are inherently a labeling process (attaching labels to physical objects, activities and events);
Such classifications may not actually be observable or possible – the link between observable features and classes may not be feasible with any technique