Adaptive Sampling and Prediction (ASAP) - PowerPoint PPT Presentation

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Adaptive Sampling and Prediction (ASAP)

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Title: Slide 1 Author: curtint Last modified by: Cindy Hanrahan Created Date: 5/25/2005 2:43:05 PM Document presentation format: On-screen Show Company – PowerPoint PPT presentation

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Title: Adaptive Sampling and Prediction (ASAP)


1
Research and Development
2006
2007
2008
2010
2004
2011
2005
2009
AOSN-II
Adaptive Sampling and Prediction (ASAP)
Autonomous Wide Aperture Cluster for Surveillance
(AWACS)
Undersea Persistent Surveillance (UPS)
Persistent Littoral Undersea Surveillance (PLUS)
Basic Research Program
Exploratory Development Program
Advanced Development Program
2
Undersea Persistent Surveillance (UPS) Stages
Opportunities
Stage 0 Ocean Nowcast / Forecast
Glider fleet
Targeted measurements
Data assimilative models
Remote Sensing
Efficient propagation models
Stage I Adaptive Search
Vector sensor arrays
Efficient intercept algorithms
Stage II Adaptive DCLT
Advanced signal processing
Cooperative behavior
Stage III Adaptive Convergence
Mobile, network control
3
Stages 0 and I Key Questions
AOSN-II
ASAP
How to obtain the best field estimates given
sparse sampling?
What advantage does targeted observation give to
predictive skill?
What advantage does a clustered, adaptive
architecture provide to predictive skill?
Environment


Feature tracking
Objects
.. .. ..
Target glimpse
What advantage does adapting to the environment
provide to detection?
What advantage does a nested, adaptive aperture
antenna provide to detection?
What gain advantage do mobile, vector arrays
provide?
AWACS
UPS
4
In-Situ Data
Remote Sensing Data
Databases
Lagrangian Fixed sensors
Mobile sensors
Adaptive Sampling Strategies
Data Assimilation
External Forcing
Models
Environment Analysis Prediction
Object Analysis Prediction
Constituent Fields Current fields Biological thin
layers
Detection, Classification, Localization, Tracking
(DCLT) Decision
Field Sampling Decision
Feedback reduces error
5
Passive Vector Sensor ArraysE-Field Sensors
Acoustic and Ocean ModelsTargeted Observations
Adaptive Feedback
Directional Sensitivity
Mobility, Persistence
Autonomous DCLT
Autonomous Underwater VehiclesAcoustic Modems
Autonomous DCL Automated Tracking
6
Example Coverage Analysis for All Gliders in
AOSN-II
  • varies 2 km (at shore) to 10 km (at 4000m
    depth), t 24 hours,
  • Outside black contour, locations not sampled for
    48 hours.
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