UNCLASSIFIED//CUBRC PROPRIETARY Advantage Through Technology.
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Transcript of UNCLASSIFIED//CUBRC PROPRIETARY Advantage Through Technology.
UNCLASSIFIED//CUBRC PROPRIETARY
UNCLASSIFIED//CUBRC PROPRIETARY
Advantage Through TechnologyAdvantage Through TechnologyAdvantage Through TechnologyAdvantage Through Technology
UNCLASSIFIED//CUBRC PROPRIETARY
UNCLASSIFIED//CUBRC PROPRIETARY
UNCLASSIFIED//CUBRC PROPRIETARY
UNCLASSIFIED//CUBRC PROPRIETARY
Information Fusion Functional Model(Jt. Directors of Laboratories (JDL), 1993)
Level 0 — Sub-Object Data Association & Estimation: pixel/signal level data association and characterization
Level 1 — Object Refinement: observation-to-track association, continuous state estimation (e.g. kinematics) and discrete state estimation (e.g. target type and ID) and prediction
Level 2 — Situation Refinement: object clustering and relational analysis, to include force structure and cross force relations, communications, physical context, etc.
Level 3 — Impact Assessment: [Threat Refinement]: threat intent estimation, [event prediction], consequence prediction, susceptibility and vulnerability assessment
Level 4: Process Refinement: adaptive search and processing (an element of resource management)
Level 0Processing
Sub-object DataAssociation &
Estimation
Level 1Processing
Single-ObjectEstimation
Level 2Processing
SituationAssessment
Level 3Processing
Mission ImpactAssessment
Level 4Processing
ProcessAssessment
Data BaseManagement System
SupportDatabase
FusionDatabase
INFORMATION FUSION PROTOTYPEJEM
JWARN3GCCS• Point and
Standoff Sensors• Data Sources• Intel Sources• Air Surveillance• Surface Sensors• Standoff Sensors• Space
Surveillance
Methods:--Combinatorial Optimization
--Linear/NL Estimation--Statistical
--Knowledge-based--Control Theoretic
TrackingAttributesID/Events
RelationshipsAggregation
Intent
LethalityCOA
Opportunity
PerformanceContext
Consistency
DetectionReports
UNCLASSIFIED//CUBRC PROPRIETARY
UNCLASSIFIED//CUBRC PROPRIETARY
State University of New York
UniversityCenterBuffalo
Engrg & Applied Sciences
Industrial & Systems Engrg CMIF
……….
CUBRC
Intl Security
Hyper-sonics
ChemBioDefense
MedicalBiotech
PublicSafety
InfoFusion
Major Research University Multidisciplinary Not-for-Profit
Shared Technical and Administrative StaffsJointly Managed
Jim Llinas, Executive DirectorMoises Sudit, Managing DirectorRakesh Nagi, ISE ChairJohn Crassidis, Associate Director
Mike Moskal, Vice President of CUBRC
UNCLASSIFIED//CUBRC PROPRIETARY
UNCLASSIFIED//CUBRC PROPRIETARY
• Applied RDT&E
Focus
• Cleared Personnel
and Facilities
• Systems
Engineering
• SW/HW
Development
• World Class Research
Personnel and
Facilities
• Focus on Basic
Research
Application Driven Research and Development for Defense, Intelligence Application Driven Research and Development for Defense, Intelligence
and Homeland Securityand Homeland SecurityGovernment Agencies (25+) Government Agencies (25+) and Industrial Partners (50+)and Industrial Partners (50+)
Development & Transition Engineering
Applications Engineering,
Fielding & Support
6.1 6.2 6.3 6.4 6.5
TRL1 TRL4 TRL5 TRL6 TRL7 TRL8 TRL9TRL2 TRL3
Universities (~30+)Universities (~30+)
• Specific Technologies
Under Development
Small Businesses (10+)Small Businesses (10+)
USAMRIID
UNCLASSIFIED//CUBRC PROPRIETARY
UNCLASSIFIED//CUBRC PROPRIETARY
• Capability to represent interactions of players at a high level of fidelity including
• Real world capability to model tracker and correlation capabilities of current and future systems
• Demonstrated interfaces to many other high fidelity models for aircraft platforms under test
• Interfaces to many other specific threat system models
• Description/Customer Needs: DIADS is an emulation fidelity model of the integrated threat air defense system. Important component in many test and training activities
• Customer: Air Force• Importance: Key simulation in the evaluation
of aircraft effectiveness• Comment: Used at training ranges and key
airborne weapons platforms evaluations
• Continuous development since 1996• Derived from a Hardware-in-the-loop simulation• Demonstrated performance in Live, Virtual and
Constructive Applications (LVC)• Used by both the test and training community • Capability to run as a mission level model but
used more frequently in a multi-model environment
UNCLASSIFIED//CUBRC PROPRIETARY
UNCLASSIFIED//CUBRC PROPRIETARY
• Mission: Information Fusion and related areas primarily but not exclusively for defense and homeland security applications
• Basic and Applied Research in:– Multiple-sensor and instrumented systems– Synergistic Human-Multisensor systems – Real-time Decision-making using Hierarchical Fusion– Graph Theory and Optimization for Level 2/3 Fusion– Multi-modal information environments (speech+text+imagery+RF sensor+human input)
• Applications:– Defense: Intelligence/Surveillance/Reconnaissance; Tactical Applications; Homeland Security– Non-Defense: Robotics; Conditioned-Based Maintenance; Medical; Transportation; Geology; Natural
Disasters/Crisis Mgmt• History and Funding:
– Started in 1996 with Air Force Research Lab Contract– Funding activity evolving; currently ~$10M/year
• Scholarly: – Long-standing member of “JDL” fusion group and First President of Intl Society for Info Fusion– Extensive publishing by CMIF PI Team including books, Jl papers, conference papers and review boards– “Critical Issues” Workshops—5 years– CMIF is unique in American Universities as a research activity focused on IF technology for DHS/DoD– Consortium development to include other universities (SU, RIT and PSU) and industrial partners and
development of a Graduate-level program in Data Fusion– Currently working on developing a consortium with TAMU and VPI as well
UNCLASSIFIED//CUBRC PROPRIETARY
UNCLASSIFIED//CUBRC PROPRIETARY
• Only Integrated Information Fusion (IF) Research Center in US Academia
• In existence 15 years• Broad range of DoD/Agency
sponsored research programs• ~15 Professor/Stakeholders• Systemic approach to IF capability
development• Unclassified to Classified, 6.1 to
Transition• Collaborations with
– PSU– RIT, Syracuse– TAMU, VPI– Buffalo:
• Ctr for Unified Biometrics• National Center for Geographic
Information and Analysis• Center for Information Systems
Assurance • Center for Document Analysis and
Recognition• Wireless and Networking Systems Lab• Semantic Network Processing Systems
Research Group
Scientific Foundations of the Data Fusion Process
RealStates in the
World
ObservationalMeans
Info. FusionProcesses
EstimatesOf World States
Dec-MkgAnalysis
etc
Data Association
EvaluationActions
Process Refinement
A Process to ESTIMATE conditions in the Real World from Observational Data
Modeling Tactical
Phenomena
SensingTechnologies
SignalPropagation
MathematicalAnd Symbolic
EstimationTechniques
SignalProcessing Human
ComputerInterfacing
HumanFactors and
HumanEngineering
DecisionScience
VisualizationVirtualReality
SensorNetworks
ControlTheory
CombinatoricOptimization
Broadly Multidisciplinary
UNCLASSIFIED//CUBRC PROPRIETARY
UNCLASSIFIED//CUBRC PROPRIETARY
UNCLASSIFIED//CUBRC PROPRIETARY
UNCLASSIFIED//CUBRC PROPRIETARY
• Capability to accept data from different domains
• Capability to accept potentially double counted data
• Cross-correlate different domains to objects under surveillance
• Build “Big Picture” with reduced workload for operators
• Description/Customer Needs: Evolving set of algorithms to process data of unknown provenance in a Net-Centric SIGINT Focused Information System (NCSFIS)
• Customer: Sierra Nevada Corporation• Importance: Process large volumes of data to
assist operators in developing situation awareness
• Comment: First integration performed. Additional capabilities in the “pipeline”
• Recent effort for CUBRC – Sept 2007• First integration involves ELINT and COMINT
pre-processed reports on static objects• Pre-integration demonstration of
measurement-level COMINT, MTI and IMINT• Interplay with Coarse-of-Action Mission-
Planning to provide real-time optimization of surveillance asset usage
UNCLASSIFIED//CUBRC PROPRIETARY
UNCLASSIFIED//CUBRC PROPRIETARY
• Problem to be solved– Fully derive relative air vehicle navigation equations
• Includes both relative position and attitude• Used quaternion for relative attitude
– Used relative LOS observations only• Texas A&M VISNAV sensor
– Applications include UAV relative navigation without GPS and fully autonomous refueling
tan tan
tan ( ),
FHG
IKJ
ffct v
F v
fct v
v ki i
i i
linear ( )
nonlin. calibration ( ) where
scaled current imbalance
1 2
1 2
• Energy centroid located more accurately than 1 part in 5,000 with proper design, calibration & signal processing, 1 part in 2,000 routinely achieved
• 1 to 5 μs rise time Can be sampled at very high frequency• With proper choice of optics, accuracy of energy centroid is a
weak function of the depth of field
UNCLASSIFIED//CUBRC PROPRIETARY
UNCLASSIFIED//CUBRC PROPRIETARY
• Problem to be solved– Consider only one set of LOS vectors between
spacecraft– Rotation around LOS vector is not known– Under-deterministic case (standard attitude
determination issue)– Can overcome problem by running filter with
motion• Convergence and observability problems need to
be overcome
– Our goal is to exploit formation information to find a deterministic solution Derived a method that handles
nonparallel beams, but also includes range errors. Less of a problem as distance increases
Deputy 2Aircraft
Deputy 1Aircraft
ChiefAircraft
Total of 2 “eqns” and3 unknowns
Total of 4 “eqns” and6 unknowns
Total of 6 “eqns” and6 unknowns
UNCLASSIFIED//CUBRC PROPRIETARY
UNCLASSIFIED//CUBRC PROPRIETARY
Posterior PDF
KF 1
KF 2
KF M
Unknown SystemReal System
MMAE Filter
• Bank of parallel filters, multiple estimates• State estimate is a weighted sum of each
filter’s estimate– Measurement residuals are “drivers” of
adaptive process– Weights derived from Bayes rule– Weights are probabilities
• Can work with nonlinear systems– EKF assumptions must be valid– Measurement residual must be Gaussian
• CMIF Extension– Generalized MMAE (GMMAE) uses a window
of residuals– Combines autocorrelation for i steps back
with MMAE– When i = 0 standard MMAE is achieved – Goal is to improve convergence– Many applications
• Current research involves L1/L2 integration
– Currently extending to UKF GMMAE• Collaborations with Simon Julier
Tracking Example with i = 4
Proc
ess
Noi
se V
aria
nce
UNCLASSIFIED//CUBRC PROPRIETARY
UNCLASSIFIED//CUBRC PROPRIETARY
MMAE
Health Monitoring and Fault Detection
Parameter
Identification
Filter Tuning
• Robust Target Tracking• Adaptive Signal Processing• Navigation applications
• Adaptive Control• Communication Systems• Tactical Assessment
• Sensor and actuator degradation faults• Structural and mechanical health monitoring• Reconfigurable (intelligent) systems• Higher level fusion applications
UNCLASSIFIED//CUBRC PROPRIETARY
UNCLASSIFIED//CUBRC PROPRIETARY
15 20 25 30 35 40 45 50 55 600
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Time (mins)
X-P
ositi
on (
3
Bou
nds)
(m
)
Fusion Node vs. Local Nodes
• Problem to be solved– Reduce single point failures in estimation system
architectures– Fuse multiple filters (nodes) in optimal manner– Use optimal platform (sensor) motion to lower
estimation errors– Fusion node (FN) uses only an estimate and
covariance from each Local node (LN)– Our objective is to provide a robust estimation
system architecture for dynamic problems• e.g. Target Tracking
FN – red (solid)LNN – all others (dashed)
15 20 25 30 35 40 45 50 55 60-2000
-1000
0
1000
2000
X -
Err
ors
(m)
Fusion Node 3 Bounds
15 20 25 30 35 40 45 50 55 60-2000
-1000
0
1000
2000
Time (mins)
Y -
Err
ors
(m)
UNCLASSIFIED//CUBRC PROPRIETARY
UNCLASSIFIED//CUBRC PROPRIETARY
Project Description Discrete Optimization Models are designed to solve the “right”
problems considering spatial as well as temporal constraints with the objective of maximizing the delivery of services by the entire fleet of UAVs:
● Maximize the servicing of certain predefined “targets”● UAVs are constrained by the amount of time they can fly before
refueling, the amount of service they can provide and a lower/upper amount of service the entire fleet should provide to a specific “target”.
● Consideration of threat, enemy attack, or natural hazards ● Novel Discrete Optimization techniques• Management of Emerging Targets:● Targets need to be classified into an appropriate priority● UAVs scheduling and Sensor-Platform Assignment
Significance
Implementation of the OPTIMAS architecture into the overall Command and Control and Combat Systems (C2 and CS) Program will greatly enhance the decision-making effectiveness of maritime operators by increasing overall situation awareness and presenting optimal or high quality solutions to a suite of strategic and tactical decision making problems. The OPTIMAS suite of tools provides feedback to the operators and to the tools themselves both within and between the strategic and tactical level issues/problems. OPTIMAS facilitates the complex decisions to be made in an atmosphere of tight resources and dynamically changing environments taking advantage of the computational strength of computers in the human-computer interaction paradigm.
Flight Dynamics – ‘Real’ and Approximated by Dubins Vehicles
Dubins Vehicles: route tracking by a kinematic vehicle moving forward only with a lower bounded turning radius
Dubins Vehicles: route tracking by a kinematic vehicle moving forward only with a lower bounded turning radius
A
B
C C
A
BBetter
Tracking
UNCLASSIFIED//CUBRC PROPRIETARY
UNCLASSIFIED//CUBRC PROPRIETARY
• Approximate the conditional pdf as a mixture of Gaussian components
– The mean and covariance of each of the Gaussian component is propagated by using the extended Kalman filter or unscented Kalman filter
• Two update schemes for the forecast weights– Continuous Dynamical Systems: minimize the
Fokker-Planck-Kolmogorov Equation (FPKE) error– Discrete Dynamical Systems: minimize the integral
square difference between the true pdf and its approximation
– During the measurement update, Bayes rule is used to update the weights
– Unique solution for weights is guaranteed
• Future Work– Automatic selection of number of minimum
number of Gaussian components required– Improve computational cost through
parallelization– Many applications
• Orbit determination, Attitude Estimation• Asteroid Collision Probability• GPS-less Localization• Plum Tracking
– Currently extending to UKF Gaussian Sum Filter
• Collaborations with Simon Julier