MURI Annual Review Meeting Randy Moses November 3, 2008

1. Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target ExploitationOverview MURI Annual Review Meeting Randy Moses November 3, 2008

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. ATE Objectives Sensor Resources ATR/ATE Inferences and Confidences Optimal, Robust Information Fusion • Graphical Models • Performance Measures • Information State Propagation • Contextual Models • Learning and Adaptation Features and Uncertainties Measurement Constraints Priors and Learned Statistics Value of Information Adaptive Front-End Signal Processing Dynamic Sensor Resource Management • Decision-directed Imaging and Reconstruction • Modeling and Feature Extraction • Statistical Shape Estimation • Dynamic sensor allocation • Efficient sensor management algorithms • Multi-level planning • Performance uncertainty Research Framework 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. Remove this slide – but it serves as a useful reminder for planning. MURI Program Statement of Work 1. 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. MURI: Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation

6. Optimal, Robust Information Fusion • Graphical Models • Performance Measures • Information State Propagation • Contextual Models • Learning and Adaptation Information Fusion: Key Research Questions Inference on Graphical Models: • Structures and algorithms for fusion, tracking, identification • Scalable algorithms • Learning and adaptation • Contextual Information How to effectively direct front-end signal processing? State propagation in graphical models. What are the ‘right’ performance measures and bounds for FE and Sensor Mgmt? MURI: Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation

7. Adaptive Front-End Signal Processing (R. Moses, lead) • Decision-directed Imaging and Reconstruction • Modeling and Feature Extraction • Statistical Shape Estimation Signal Processing: Key Research Questions Feature sets and feature uncertainties that permit fusion across modalities Problem formulations that admit context, priors and directed queries • Physics-based feature representations • Decision-directed imaging and reconstruction • Feature uncertainty characterization • Statistical shape estimation • Adaptation and Learning MURI: Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation

8. Dynamic Sensor Resource Management (D. Castañón, lead) • Dynamic sensor allocation • Efficient sensor management algorithms • Multi-level planning • Performance uncertainty Sensor Management: Key Research Questions • Integrate ATE performance based on graphical models • Manage evolution of “information state” in support of ATE missions • 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 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. 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. 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. 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 MURI: Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation

11. 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 industry RLM: 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 MURI: Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation

15. ATE MURI Web Page • People • Publications • On-line research collaboration space • Code • Data resources MURI: Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation

16. 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) MURI: Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation

17. 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) MURI: Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation

18. Student Theses Probably delete this slide 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. 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?? MURI: Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation

20. 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 MURI: Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation

21. 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 Optimal, Robust Information Fusion in Uncertain Environments (Willsky) Graphical Models for Resource-Constrained Hypothesis Testing and Multi-Modal Data Fusion (Castañón, Fisher, Hero) Sparse Reconstruction and Feature Extraction (Çetin, Ertin, Karl, Moses, and Potter) Algorithms and Bounds for Networked Sensor Resource Management (Castañón, Fisher, Hero) Tools for Analyzing Shapes of Curves and Surfaces (Fisher, Srivastava) MURI: Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation

23. Agenda MURI: Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation