muri: integrated fusion, performance prediction, and sensor management for automatic target...
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MURI: Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation 1
Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation
Overview
MURI Annual Review Meeting
Randy Moses
November 3, 2008
MURI: Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation 2
Research Goal
Develop an integrated systems theory that jointly treats information fusion, control, and adaptation for automatic target exploitation (ATE). Multiple, dynamic sensors Multiple sensing modes Resource-constrained environments
MURI: Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation 3
Research Framework
ATE ObjectivesSensor Resources
ATR/ATE Inferencesand ConfidencesOptimal, Robust Information
Fusion
•Graphical Models•Performance Measures•Information State Propagation
•Contextual Models•Learning and Adaptation
Dynamic Sensor Resource Management
• Dynamic sensor allocation• Efficient sensor
management algorithms• Multi-level planning• Performance uncertainty
Adaptive Front-End Signal Processing
• Decision-directed Imaging and Reconstruction
• Modeling and Feature Extraction
• Statistical Shape Estimation
Features andUncertainties
Priors andLearned Statistics
MeasurementConstraints
Value ofInformation
MURI: Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation 4
Research Framework
Inference-rooted metrics provide the common language
across the system likelihoods
posterior probabilities information measures
MURI: Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation 5
MURI Program Statement of Work1. Innovative Front-End Processing. We will develop robust, adaptive algorithms for decision
directed front-end waveform processing. We will provide analytical performance assessments and performance bounds for these approaches. Specifically, we will develop
1a) decision-directed imaging and reconstruction techniques.1b) physics-based parametric feature representations of targets and confusers, and corresponding
feature extraction algorithms and uncertainty metrics.1c) statistical shape theory that integrates with regularized inversion techniques.2. Optimal, Robust Information Fusion in Uncertain Environments. We will develop
fundamental theory and associated algorithms for inference and performance prediction in ATE systems that employ distributed, multi-modal sensing. We will develop hierarchical structures that integrate both signal-level features and feature uncertainties with high-level context. Specifically, we will develop
2a) structured inference methods for graphical models that manage complexity in high-dimensional ATE inference problems.
2b) information-theoretic performance measures for ATE inference assessment and for multimodal data fusion.
2c) methods for approximating and propagating statistical state information under communications and computational resource constraints.
2d) machine learning methods for on-the-fly adaptation of inference structures to evolving target and context scenarios.
2e) contextual models for inference in complex ATE clutter environments.3. Dynamic Sensor Resource Management for ATE. We will develop a fundamental theory and
associated algorithms for on-line, adaptive, active sensor management to support ATE using distributed, multi-modal sensing. Specifically, we will develop
3a) sensor management algorithms that incorporate ATE inference performance metrics.3b) theory, models and associated algorithms for dynamic sensor management of ATE operations
using multiple multi-modal, passive, and active sensing platforms.3c) joint trajectory optimization and sensor control algorithms that optimize ATE inference metrics.3d) sensor management algorithms that are robust against unknown or inaccurate models of sensor
performance.
Remove this slide – but it serves as a useful reminder for planning.
MURI: Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation 6
Information Fusion: Key Research Questions
Optimal, Robust Information Fusion
•Graphical Models•Performance Measures•Information State Propagation
•Contextual Models•Learning and Adaptation
Inference on Graphical Models:
• Structures and algorithms for fusion, tracking, identification
• Scalable algorithms• Learning and adaptation• Contextual Information
What are the ‘right’ performance measures and bounds for FE and
Sensor Mgmt?
State propagation in
graphical models.
How to effectively direct front-end
signal processing?
MURI: Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation 7
Signal Processing: Key Research Questions
Adaptive Front-End Signal Processing (R.
Moses, lead)• Decision-directed Imaging and Reconstruction
• Modeling and Feature Extraction
• Statistical Shape Estimation
• Physics-based feature representations
• Decision-directed imaging and reconstruction
• Feature uncertainty characterization
• Statistical shape estimation • Adaptation and Learning
Feature sets and feature uncertainties that permit fusion across modalities
Problem formulations that admit context, priors and
directed queries
MURI: Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation 8
Sensor Management: Key Research Questions
Dynamic Sensor Resource Management (D. Castañón, lead)
• Dynamic sensor allocation• Efficient sensor
management algorithms• Multi-level planning• Performance uncertainty
• Active sensor control that incorporate ATE performance metrics
• Multi-modal, heterogeneous platforms
• Scalable real-time algorithms for theater-level missions
• Robust to inaccurate performance models
• Integrate ATE performance based on graphical models
• Manage evolution of “information state” in support of ATE missions
MURI: Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation 9
MURI Payoff
Goal: Develop an integrated theory for ATE systems that combines information fusion,
platform control, signal processing, and adaptation.
Payoff:• Systematic design tools for end-
to-end design of multi-modal, multi-platform ATE systems.
• Active platform control to meet ATE objectives.
• System-level ATE performance assessment methods.
• Adaptive, dynamic ATE systems.
Research Outcomes:• An integrated theoretical
framework for dynamic information exploitation systems.
• Theoretical foundations for adaptivity and learning in complex inference systems.
• New algorithms and performance metrics for coupled signal processing, fusion, and platform control.
MURI: Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation 10
MURI Team
UNIVERSITY TEAM: Ohio State University (lead)
Randy Moses (PI) Lee Potter Emre Ertin
Massachusetts Institute of Technology Alan Willsky John Fisher Mujdat Çetin (also Sabanici U.)
Boston University David Castañón Clem Karl
University of Michigan Al Hero
Florida State University Anuj Srivastava
AFOSR: David Luginbuhl Doug Cochran
AFRL POC: Greg Arnold
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Year 1 Meeting Feedback
Strongly positive on team expertise and interactions.
Strongly supportive of research plan Maintain emphasis on fundamental
research. Maintain research continuity
Interactions among the MURI PIs Interactions with government labs and
industryRLM: Update these!
MURI: Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation 12
Year 2 Advances
Regularized Tomography for Sparse reconstruction
Sparse apertures – monostatic and multistatic Sparse ‘objects’ (targets or scenes) Anisotropy characterization 3D Reconstruction for wide angle and circular SAR Decision-directed reconstruction Lots of cross-pollination, especially OSU, BU, MIT
Shape Statistics for Curves and Surfaces Shape Analysis
Shape distribution; not just point estimates Bayesian Classification from Shapes
MURI: Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation 13
Year 2 Advances II
Sensor Management: Multistatic radar sensor management using scattering
physics Radar resource management with guaranteed uncertainty
metrics. Real-time SM algorithms and performance bounds
Scalable, flexible inference Lagrangian relaxation and multiresolution methods for
tractable inference in graphical models New graphical model-based algorithms for multi-target,
multi-sensor tracking Learning Graphical Model structures directly for
discrimination GM-based distributed PCA and hyperspectral image
discrimination
MURI: Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation 14
MURI Students
10 graduate students and 1 postdoc fully supported by the MURI.
14 graduate students and 1 postdoc working on the MURI team with outside support (e.g. fellowships) or partial funding.
3 PhD and 3 MS degrees awarded in Year 1 RLM Update for Year 2
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ATE MURI Web Page
People Publications On-line research
collaboration space Code Data resources
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Intra-team Interactions
MURI research meeting in Boston Dec07 30 participants, 25+ posters,
Sensor Management Book Collaboration Foundations and Applications of Sensor Management; Springer
2007; Editors: A. Hero, D. Castañón, D. Cochran, K. Kastella Student Committees
D. Castañón (BU) on Jason Williams’ (MIT) dissertation committee J. Fisher (MIT) on Shantanu Joshi’s (FSU) dissertation committee
Numerous Research Interaction Visits A. Hero → MIT sabbatical Au06 Fisher → FSU; Joshi Ph.D. committee Moses → 3 visits to MIT Summer 2006 Fisher → UMich July07 Çetin → OSU Aug07 (video-conference)
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Publications and Student Degrees
129 Publications 2 book chapters 45 Journal publications 82 Conference proceedings papers
Student Graduate Degrees 6 Ph.D. graduates
Williams (MIT), Rangarajan (UMich), Joshi (FSU), Malioutov (MIT), Johnson (MIT), Bashan (UMich)
6 M.S. graduates Austin (OSU), Som (OSU), Varhney (MIT), Chen
(MIT), Choi (MIT), Chandrasekaran (MIT)
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PHD E. Bashan, Efficient Resource Allocation Schemes for Search. PhD thesis, UMich, 2008. D. Malioutov, Approximate inference in Gaussian graphical models. PhD thesis, MIT, Cambridge,
MA, 2008. R. Ranagarajan, Resource constrained adaptive sensing, PhD Thesis, UMich, 2007. J. Johnson, Convex Relaxation Methods for Graphical Models: Lagrangian and Maximum-Entropy
Approaches. PhD thesis, MIT, Cambridge, MA, 2008. J. L. Williams, Information Theoretic Sensor Management. PhD thesis, MIT, 2007. S. Joshi, Inference in Shape Spaces with Applications to Computer Vision , Ph.D. thesis, FSU,
2007
MS Z. Chen, Efficient multi-target tracking using graphical models," Master's thesis, MIT, Cambridge,
MA, 2008. M. Choi, “Multiscale gaussian graphical models and algorithms for large-scale inference,“
Master's thesis, MIT, Cambridge, MA, 2007. N. Ramakrishnan, “Joint enhancement of multichannel synthetic aperture radar data," Master's
thesis, The Ohio State University, Mar. 2008. V. Chandrasekaran, “Modeling and estimation in gaussian graphical models: Maximum-entropy
relaxation and walk-sum analysis," Master's thesis, MIT, Cambridge, MA, 2007. K. Varshney, “Joint image formation and anisotropy characterization in wide-angle SAR,”
Master’s thesis, MIT, Cambridge, MA, May 2006. C. D. Austin, “Interferometric synthetic aperture radar height estimation with multiple scattering
centers in a resolution cell,” Master’s thesis, The Ohio State University, 2006.
Student ThesesProbably delete this slide
MURI: Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation 19
Research Leadership
Journal Special Issue Statistical Shape Modeling, IEEE PAMI, 2008 (Srivastava)
Themed Workshops Workshop on Challenges and Opportunities in Image
Understanding, Jan 2007 (Srivastava) Workshop on Signal and Information Processing, July 2008
(Hero) Conference Special Sessions
Sparse Reconstruction methods in Radar SPIE Defense Symposium, April 2008 (Moses)
Signal Processing for Inference IEEE 5th DSP Workshop, January 2009 (Moses)
Signal Processing and Learning for Sensor Signal Exploitation 2008 Asilomar Conference on Signal, Systems, and Computers,
Oct 2008 (Ertin)
Fisher: Statistical Signal Processing Workshop 2007??
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Interactions and Transitions I Presentations at 15 conferences Scientific Advisory Boards
Castañón: Air Force SAB Hero: ARL TAB Willsky: DARPA POSSE; Willsky: Chief Scientific Consultant to BAE-AIT Hero: National Research Council
Research Formulation Workshops Srivastava organized ARO Workshop on Challenges and
Opportunities in Image Understanding Moses, Fisher, Hero, Arnold presented
Hero organized ARO Workshop on Signal and Information Processing
Moses, Fisher, Castañón presented Ertin: AFRL ATR Modeling Workshop
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Interactions and Transitions II
Transitions: Sparse aperture research -> AFRL GREP program Srivastava statistical shape analysis to Northrup-
Grumman via Innovation Alliance Award A. Hero directed Mike Davis (GD-AIS) research on
MIMO radar networks MURI LADAR model identification and radar
scheduling provided to Lincoln Laboratory Summer internships
Julie Jackson: AFRL Dayton, Summers 06 and 07 Kerry Dungan: AFRL Dayton, Summer 07 Ahmed Fasih, SET Corp. Dayton, Summers 06 and 07 Christian Austin: SET Corp. Dayton, Summer 06 Kyle Herrity: FAAC, Inc Ann Arbor MI, Summer 07
MURI: Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation 22
Today’s Presentations
Algorithms and Bounds for Networked Sensor Resource Management (Castañón, Fisher, Hero)
Optimal, Robust Information Fusion in Uncertain Environments (Willsky)
Sparse Reconstruction and Feature Extraction (Çetin, Ertin, Karl, Moses, and Potter)
Graphical Models for Resource-Constrained Hypothesis Testing and Multi-Modal Data Fusion (Castañón, Fisher, Hero)
Tools for Analyzing Shapes of Curves and Surfaces (Fisher, Srivastava)
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Agenda
8:30 - 9:00 Intro and Overview (Moses)
9:00 – 9:40 Optimal, Robust Information Fusion in Uncertain Environments (Willsky)
9:40 - 10:20
Sparse Reconstruction and Feature Extraction (Potter)
10:20 - 10:40
Break
10:40 - 11:10
Bayesian Fusion of Features in Images for Shape Classification (Srivastava)
11:10 - 11:50
Graphical Models for Resource-Constrained Hypothesis Testing and Multi-Modal Data Fusion (Fisher)
11:50 - 12:50
Lunch - Blackwell Ballroom
12:50 - 1:30
Algorithms and Bounds for Networked Sensor Resource Management (Castañón )
1:30 - 2:00 Summary (Moses)
2:00 - 2:30 Government caucus
~2:30 Feedback and Discussion