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

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Research Framework

Inference-rooted metrics provide the common language

across the system likelihoods

posterior probabilities information measures

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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.

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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?

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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

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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

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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.

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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!

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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

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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

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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

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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

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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