t opic 13: l evel 4 david l. hall. t opic o bjectives introduce level-4 processing (process...
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TOPIC 13: LEVEL 4
David L. Hall
TOPIC OBJECTIVES
Introduce Level-4 processing (process control) Describe two broad approaches for Level-4
processing Optimization techniques Market based methods
Provide insight into new research areas
LEVEL 4 PROCESSING (PROCESS REFINEMENT)
MOTIVATION/EVOLUTION OF LEVEL-4 PROCESSING
Intelligent sensor/processor systems – rapid advances provide opportunity for increased dynamic tasking and control (e.g., agile sensors, advanced waveforms, look-angle computations, etc)
Distributed, dynamic network systems – increased complexity of network systems with heterogeneous, distributed sensors and processors
Increased bandwidth – new opportunities for strategies for passing data and commands throughout an environment
Service based architectures - network services provide dynamic insertion of new sensors, models, processing services
Complex algorithms – new agile algorithms provide potential for hybrid, parallel, multi-model approaches
JDL Level 4 Process: Process Refinement
Mission Management
Target Prediction
Sensor & Platform Modeling
System Performance Modeling
System Control
Representation of mission objectivesMission constraintsAdjudication between fusion optimization & mission constraint
Target state estimation
Target attribute modeling
Sensor platform modelsSensor characteristicsSignal propagation modelsTarget/sensor signal interaction
Sensor performance modelsAlgorithm performance modelsMeasures of performanceMeasures of effectiveness
Optimization criteria
Optimization algorithm(s)
Control philosophy
SENSOR MANAGEMENT TECHNIQUES
is
Sensor Management
does
A process which seeks to manage and coordinate the use of sensing resources in a manner that improves the processof data fusion and ultimately that of Perception synergistically
•Automation of sensor allocation, pointing, moding and emission
•Prioritization and scheduling service requests
•Assistance in sensor data fusion
•Support of reconfiguration and degradation
•Optimization of the use of the available sensor assets
•Assistance in Communication
Based on
1. Heuristics2. Expert Systems 3. Utility Theorem 4. Automated control theory5. cognition6. decision theoretic approaches 7. probability theory 8. stochastic dynamic programming 9. linear programming 10. neural networks 11. genetic algorithms 12. information theory
EXAMPLE OF DESIGN AND EVALUATION OF SENSOR MANAGEMENT SYSTEMS
MOTIVATIONMOTIVATION
ENVIRONMENT PLATFORM CAPABILITY
Dynamic Jamming/Deception Low Observables Numbers/Spacing/Combinatorics
Integrated Avionics Agile/Progammable/Steerable Sensors
limited by
Insufficient sensor resources Varied sensor capability/performance
- Limited processing- Limited power
Unplanned sensor failures Mission constraints (e.g., EMCON)
LEVEL FOUR PROCESSING:PROCESS REFINEMENT
SensorsSensor
Processing
FusionProcessing
Levels
ΣMissionRequirements
MISSION OPERATIONS(e.g. Weapon System) Combat
Environment
EVALUATION
Σ
Fusion Control
Source Requirements
SOURCE AND FUSION CONTROL LOOP
Information Requirements
Info
MISSION MANAGEMENT LOOPMission Effects
MeasurePerformance
and Effectiveness
MOE
MOP
Process Control
DESIGN ELEMENT COUPLING/COMPLEXITY
SENSOR MANAGEMENTSENSOR MANAGEMENTSYSTEMSYSTEM
• Scheduling Parameters• Sensor Synergisms• Areas of Optimum Performance
SENSOR SUITESENSOR SUITE
• Sensor Parametric Agility• Performance Specifications• Contribution to State
SENSOR DATA FUSION SENSOR DATA FUSION PROCESSINGPROCESSING
• Alignment Requirements• Association Options• Tracking Requirements/Options• Computational Aspect
o
o
REQUIREMENTS/PRIORITIESREQUIREMENTS/PRIORITIES
MULTI-SENSOR MANAGEMENT AND UTILITY
SIMILAR TO THE CASE OF AN AGILE SINGLE SENSORSIMILAR TO THE CASE OF AN AGILE SINGLE SENSOR
CONTROL VARIABLES UTILITY GOAL
Time or points on target
Sampling rate
Measured parameters
Search/track trade-offs
Covertness
Maneuvering target characteristics
Target density
High threat target present
Target detection performance
Tracking accuracy
SEARCH UTILITY F[N detected / N undetected]
TRACKING UTILITY CALC COV / DESIGN COV
GENERAL SENSOR CONTROLS
CATEGORY CONTROL FUNCTIONS INPUT TO SENSOR
Mode Control Functions On/Off control Sensor mode selection:
Power level (active sensors) Waveform or processing mode (long-range search, high resolution, NCTR, etc.) Scan, track, or track-while-scan
Sensor processing parameters: Decision thresholds Detection, track, ID criteria
Spatial Control Functions Pointing coordinates (center of field of view (FOV)) Field of view selection Scan/search rate Scan/search pattern select Parameters to control individual sectors:
Sector coordinates Modes within sector
Parameters for designated targets: Target or track index (number) Coordinates or search volume Mode to be used Predicted time of appearance Dwell time on target
Temporal ControlFunctions (timing)
Start/stop times for modes and sector control Specified sensor look time Specified dwell time on target and search Maximum permissible emission duty cycle
Reporting Control Report filters based on target attributes: Friend, foe, or both Filter by class or type Filter by lethality
Report filters based on spatial attributes: Min/max range limits Altitude layer filters Spatial region filters (sectors and volumes defined by geometry)
Priority of designated targets (by index)
FACTORS IN SETTING TARGET PRIORITY
PRIORITY FACTORCATEGORIES
SPECIFIC FACTORS & CRITERIA
IDENTITY Target Allegiance (friend, foe, or neutral) Target type or class Target lethality
INFORMATION NEED Spatial location accuracy Target identification status Track state estimate accuracy Detected targets: – Track filter covariances – Influences revisit rates required to achieve a specified tracking accuracy
– Search volumes, sectors:– Scan or dwell period requirements to achieve a specified detection/ intercept probability
THREAT (DEFENSIVE FACTORRELATIVE TO HOSTILE,UNKNOWN PRESUMED HOSTILETARGETS)
Target type identity Range to target (R) Range rate Range/range rate (time-to-go or imminence) Target lethality (a function of target type) Geometry of own ship relative to target’s weapon envelope(s)
OPPORTUNITY (OFFENSIVEFACTOR RELATIVE TO POSITIVEIDENTIFIED HOSTILE TARGETS)
Geometry of target relative to own (or other friendly) weapon envelopes Range relative to own weapon envelopes Time-to-go until target reaches detection/engagement point Probability of own ship detection by target
FIRE CONTROL NEEDS State of sensor/weapon commitments against target (lock, track, in-flight, etc.) Time-to-go for command-guided weapons in flight
GENERAL SENSOR MANAGEMENT FUNCTIONS
External Controls
EventPrediction
TargetPrioritization• Threat• Opportunity• Information Need
Sensor Prediction
Sensor Performance
Models
Spatial-TemporalCoverage Control
SituationAssessment
Level 2, 3Fusion
Level 1Data Fusion
TargetDataBase
SearchPriorities
Spatial-TemporalParameters
Capacity
Sensor- Target
Assignment
Allocation, Scheduling and ControlTarget
PrioritiesObjectivesFunctions
Control
DataStatus
Sensor Manager Functions
ManualPriorities
EMCon Cues
EMCon Cues
SensorInter face
SensorPerformance
SensorStatus
Walt, E. and Llinas, J., Multisensor Data Fusion”, Artech House, Boston, MA, 1990.
POSSIBLE MOP/MOE LIST FOR EVALUATION OF SENSOR MANAGEMENT
PROCESSDetection and Leakage: Range at target detection Percent of aircraft detected at 100 nmi Percent of aircraft detected at 50 nmi Percent of aircraft detected at 25 nmi Number of undetected targets in the field of regard Percentage of detected targets in the field of regard Time in sensor field of regard prior to detection
Target Tracking: Track variance for hostile aircraft Track variance for priority targets Track variance for all aircraft Number of tracks divided by number of targets Number of track miscorrelations Number of dropped tracks Number of dropped tracks excluding tracking leaving the field of
regard Revisit frequency for priority targets Revisit frequency for hostile targets Revisit frequency for all aircraft Percent of hostile aircraft with launch quality tracks Number of targets tracked divided by the system tracking capacity
Identification: Percent of hostile aircraft correctly identified Range of ID for hostile aircraft Range of ID for all aircraft Time in sensor field of regard prior to identification
Raid Assessment: Percent of targets correctly raid assessed Estimated targets in track/actual targets in track Time in sensor field of regard prior to raid
assessment
Kill Assessment: Percent of targets correctly assessed as killed Percent of targets correctly assessed as alive
Emissions: Total power emitted Power emitted per unit time Number of enemy RWR detections of ownship
Sensor Utilization: Percent of sensor time idle Sensor time spent reacquiring tracks Number of impossible sensor attempts Number of unsuccessful sensor attempts Percent of redundant sensor applications
System Response: Task turn around time Critical request response time Number of starved tasks
Computational Performance: MIPS utilized Memory utilized Bus bandwidth utilized
E-BUSINESS CONCEPTS FOR LEVEL 4
Leveraged Leveraged Information Information TechnologiesTechnologies
• Internet Auctions Internet Auctions & Markets& Markets
• E-CommerceE-Commerce
• Intelligent AgentsIntelligent Agents
• Distributed Distributed Resource Resource AllocationAllocation
E-business concepts are being applied to resource management in multi-sensor systems
MULTIPLE CONSUMER, MULTIPLE SENSOR SCENARIO
processorBattery
Transmission Channel
Bandwidth
Sensor
Manager
Information
Consumers
Information
Suppliers
A ONE TO ONE MAPPING! (ALMOST)
Money
Market
Seller One
Consumer
Consumer
Consumer
Seller Two
Seller Three
AUCTIONS
•Elaborate mechanisms for true consumer preference elicitation
• Sensor network Environment is a co-operative environment
• Appealing mechanism: But other complications exist
WHY STANDARD AUCTION MECHANISMS ARE NOT DIRECTLY
APPLICABLE: CASE 1
Consumer
Consumer
Scan Target at rate r1
Scan Target at rate r2
>r1
Target
Scan a
t r2
Case 1: A single supplier can satisfy multiple consumers with a single product/service
Example: Single item (Scan at rate 2) satisfies both consumers => Each item can be allocated to multiple consumers
Consumer
Consumer
Search Grids 1 to
100
Search Grids 50 to
150
Search
Grids
50 to
100
Case 2: The optimal market solution might require a bid that does not exist.
Example: The sensor capability cannot fully satisfy either consumer scan request.
WHY STANDARD AUCTION MECHANISMS ARE NOT DIRECTLY APPLICABLE: CASE 2
A MARKET-BASED DESIGN
Based on research by T. Mullen et al
Transmissionchannels
Bandwidth
Sensors
Sensor
pointing
Battery power
Processing power
Sellers
Produce
Finished goods Environmental scans, target tracks, .
Consume Consumers
Market Auctioneer
SensorManager
Schedule
Mission Manager
Budgets & Task responsibilities
Mission reports
bid
MARKET MECHANISMS
is
Market Mechanisms
Related to
Design and implementation of resource allocation problems based on some pricing system
Price Equilibrium
Market Conditions whereDemand equals supply
Resource Allocationoptimum
Rely on
is
•Price Tatonement:InherentlyAnytime
•Quantity Tatonement:Anytime adaptationAvailable
calculated
using
REMARKS ON SENSOR MANAGEMENT
Infrastructure technologies will permit integrated, ever-smarter Infrastructure technologies will permit integrated, ever-smarter system architecturessystem architectures This leads to feasibility of sensor management
Sensors are required by more than just the fusion processorSensors are required by more than just the fusion processor This creates contention for sensor service
In an integrated system, coupling exists betweenIn an integrated system, coupling exists between sensor management--fusion--sensors
This leads to a difficult global optimization problem In general, this subject is under-researchedIn general, this subject is under-researched
TOPIC 13 ASSIGNMENTS
Preview the on-line topic 13 materials Read chapter 8 of Hall and McMullen (2004) Read Mullen, et al article (2006)
DATA FUSION TIP OF THE WEEK
Situational Awareness is vitally connected with the effective control of the data fusion process from pointing and controlling the sensors to dynamic selection and control of algorithms to guiding an analyst/user attention
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