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Sequential Adaptive Sensor Management

Sequential Adaptive Sensor Management. Alfred O. Hero III † Dept. of Electrical Engineering and Computer Science, The University of Michigan. Progress (since June 05) Theory of information gain (IG) scheduling Result : IG bounds risk (Kreucher CDC05).

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Sequential Adaptive Sensor Management

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  1. Sequential Adaptive Sensor Management Alfred O. Hero III† Dept. of Electrical Engineering and Computer Science, The University of Michigan 4th Year ARO MURI Review June, 2006

  2. Progress (since June 05) Theory of information gain (IG) scheduling Result: IG bounds risk (Kreucher CDC05). Implication: IG is a universal surrogate Classification reduction for RL Result: Generalization error bounds (Blatt, Thesis-06). Implication: Minimum # samples and model/measurement complexity Adaptive energy allocation and waveform selection Result: LARS reduction of optimal adaptive waveform selection policy (Rangarajan:ICASSP06) Implication: linear-complexity solution to exponential-complexity problem IRIS: sensor management for STW Result: IRIS adaptive illuminator placement strategy w/ confidence maps Implication: Information-directed path planning for STW (Marble:06) Sequential Sensor Resource Allocation Predict performance for each possible sensing action Time update information state under each available action model Compute expected improvement for each sensing action Deploy action with best predicted performance improvement Measurement update info state 4th Year ARO MURI Review June, 2006

  3. Progress Highlighted Today • Adaptive energy allocation and waveform selection [Rangarajan&Raich&Hero] • Iterative Redeployment of Illumination and Sensing (IRIS) for STW [Marble&Raich&Hero] 4th Year ARO MURI Review June, 2006

  4. Progress 1: Adaptive Waveforms • Sequentially illuminate a medium and measure backscatter using an array of sensors. • Applications to mine detection, ultrasonic medical imaging, foliage penetrating radar, nondestructive testing, communications, and active audio. • GOAL: Optimally design a sequence of waveforms using an array of transducers • To image a scatter medium (Estimation). • To track targets (Tracking) • To discover strong scatterers (Detection). 4th Year ARO MURI Review June, 2006

  5. Progress 1a: Energy allocation for D&E • Let past observations be 2.Active Waveform Design 1.Adaptive Energy Management under average energy constraint • Energy allocation question: Given transmission of certain waveforms , how much can we gain through optimal energy allocation between various time steps (Rangarajan:2005)? 4th Year ARO MURI Review June, 2006 R. Rangarajan, R. Raich and A. O. Hero, "Optimal experimental design for an inverse scattering problem,"ICASSP-2005.

  6. Gains more than 5dB!!!! • RESULT : We prove through optimal energy designs, we can achieve at least 5dB gain (compared to one-step strategy) for estimation problems (imaging). • How much can we gain for target detection?? 4th Year ARO MURI Review June, 2006

  7. Results for target detection • Two-step energy design procedure • ~ 2dB gain or 20% decrease in average error for same SNR. • How much improvement can be achieved asymptotically with time? (Work in progress) 4th Year ARO MURI Review June, 2006

  8. Progress 1b: Active waveform selection • M possible waveforms • Can only send p out M, p < M+1 • Design criterion: • Optimal solution: subset selection, is intractable 4th Year ARO MURI Review June, 2006

  9. Simplification via rule ensembles • We approximate the decision statistic at receiver (detector, estimator, classifier) by a weighted sum of non-linear functions (rule ensembles (Friedman:2005)) of subsets of q measurements at time t • Special case (GAM) for estimating state variable s • Reduced GAM waveform selection criterion Friedman, J. H. and Popescu, B. E. "Predictive Learning via Rule Ensembles." (Feb. 2005) 4th Year ARO MURI Review June, 2006

  10. Solution via convex relaxation • Convex relaxation (Tibshirani:1994) of waveform design criterion (Rangarajan:2006) Tibshirani, R. "Regression selection and shrinkage via the lasso" Technical Report (June. 1994). R. Raghuram, R. Raich and A.O. Hero, "Single-stage waveform selection for adaptive resource constrained state estimation," IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Toulouse France, 2006. 4th Year ARO MURI Review June, 2006

  11. Summary comparisons • HMM diffusion with bi-level variance • Diffusion measured in Gaussian additive noise with one of possible subsets of n=5 waveforms 4th Year ARO MURI Review June, 2006

  12. Numerical results Future Directions • Sensor network localization/tracking problem. • Combine optimal energy allocation with waveform selection. (Work in progress) 4th Year ARO MURI Review June, 2006

  13. Progress 2: Iterative Redeployment of Illumination and Sensing (IRIS) Elements of IRIS strategy • Initial illumination with physical antenna array • Antenna array is deployed at an initial location and illuminates the region of interest. • Sparse reconstruction image reconstruction (Ting:2006) is performed • Form Confidence Map of Image • Confidence map (Raich:2005) is computed using initial image and side information • Select a region of low confidence from confidence map • Simulate external energy/resolution field induced byvirtual transmitter • Place virtual transmitter in low confidence region and apply FEM, MoM, PO to estimate electric field distribution outside the building • Compute induced energy or gradient field (wrt perturbation of virtual transmitter location) • Re-illuminate with physical antenna array at maximum of simulated field M. Ting, R. Raich and A.O. Hero, "Sparse image reconstruction using a sparse prior," ICIP 2006 R. Raich and A.O. Hero, "Sparse image reconstruction for partially unknown blur functions," ICIP 2006 4th Year ARO MURI Review June, 2006

  14. IRIS Illustration: Sensor Illumination Chair Table Initial Sensor Position/Configuration Transmitter Sink Weapons Cache Point Scatterer Wall 4th Year ARO MURI Review June, 2006

  15. IRIS Illustration: Confidence Map Iterative image reconstruction (Fessler&Hero:TIP95) Sparsity constrained deconvolution (Nowak&etal:TSP03) Image confidence maps (Raich&Hero:ICASSP06) Sink Weapons Cache Wall Low confidence region 4th Year ARO MURI Review June, 2006

  16. Possible sensor locations IRIS Illustration: Virtual back-illumination Sink Weapons Cache Wall 4th Year ARO MURI Review June, 2006

  17. IRIS Illustration: Predict Energy/Resolution MAP Sink Weapons Cache Wall 4th Year ARO MURI Review June, 2006

  18. Sparse image reconstruction and confidence mapping • MAP-EM Formulation • Separates deconvolution from denoising: • EM-MAP iterations for image x and confidence map • Properties • Iterates monotonically increase likelihood • Deconvolution (E) only involves adjoint of forward operator • Fast implementation with wavenumber migration approx for H 4th Year ARO MURI Review June, 2006 Ting,M, Hero,A.O., “Sparse Image Reconstruction Using a Sparse Prior,” ICIP 2006.

  19. Sparse Reconstruction Example 4th Year ARO MURI Review June, 2006

  20. IRIS illustration for STW Initial illuminator location 1m aperture Chair Table Weapons Cache Empty Space interior Sink Accessible Region Simulated Scene Inaccessible Region Inaccessible Region External Wall – Permittivity: 10 Thickness: 0.2m Length: 10m Accessible Region 4th Year ARO MURI Review June, 2006

  21. IRIS for STW – Iteration 1: Sparse reconstruction Standard Wavenumber Migration Sparse iterative Reconstruction (10 iterations) (Marble&Raich&Hero:06) 1m Aperture 1m Aperture 4th Year ARO MURI Review June, 2006

  22. IRIS for STW – Iteration 1: Confidence Mapping Sparse Prior w = 0.25 a = 0.5 • Confidence Map shows pixels that have high confidence of being “empty space”. • Quantitative map: Ambiguous pixels 4th Year ARO MURI Review June, 2006

  23. IRIS for STW – Iteration 1: Insert virtual transmitter and simulate field IG-optimal 1m sensor placement Energy-optimal 1m sensor placement Virtual Transmitter Virtual Transmitter KL Mapping Energy Mapping 4th Year ARO MURI Review June, 2006

  24. 1 2 3 Spectral Information Gain KL Divergence – Information Gain Reference Field Observation Location • Electric Field From • Transmitter k. Horizontal Perturbation Field Virtual Transmitters Div Map= Div(E1,E3)+ Div(E1,E2) Vertical Perturbation Field KL Divergence is a measure of Discrimination Error Probability 4th Year ARO MURI Review June, 2006

  25. IRIS for STW – Iteration 2: Insert virtual transmitter and simulate field Energy-optimal 1m sensor placement Info-optimal 1m sensor placement Virtual Transmitter Virtual Transmitter 4th Year ARO MURI Review June, 2006

  26. Virtual Transmitter Virtual Transmitter Virtual Transmitter IG-optimal 1m sensor placement Energy-optimal 1m sensor placement IRIS for STW – Iteration 3: Insert virtual transmitter and simulate field Energy Mapping 4th Year ARO MURI Review June, 2006 Cross Range [m]

  27. IRIS for STW – Comparisons to fixed aperture 1m 1m 3 2 1 4 1m Aperture 10m Aperture 1m 4th Year ARO MURI Review June, 2006

  28. Personnel on A. Hero’s sub-Project (2005-2006) • Raviv Raich, post-doctoral researcher • BS Tel Aviv University • PhD Georgia Tech, May 2004 • Neal Patwari, post-doctoral researcher • BS Virginia tech • PhD, Univ of Michigan, Sept. 2005 • Doron Blatt, 4th year doctoral student • BS Univ. Tel Aviv • PhD Univ of Michigan, May 2006 • Raghuram Rangarajan, 5th year doctoral student • BS IIT Madras • Dept. Fellowship/MURI GSRA • Jay Marble, 5th year doctoral student • BS UIUC • MURI GSRA • Presently employed at NVRL 4th Year ARO MURI Review June, 2006

  29. Pubs Since June 2005 • Theses of students funded on MURI • "Performance Evaluation and Optimization for Inference Systems: Model Uncertainty, Distributed Implementation, and Active Sensing," PhD Thesis, The University of Michigan, May 2006. • Journal • “Adaptive Multi-modality Sensor Scheduling for Detection and Tracking of Smart Targets”, C. Kreucher, D. Blatt, A. Hero, and K. Kastella, Digital Signal Processing,vol. 15, no. 4, July 2005. • "Multitarget Tracking using the Joint Multitarget Probability Density," C. Kreucher, K. Kastella, and A. Hero, IEEE Transactions on Aerospace and Electronic Systems, 39(4):1396-1414, October 2005 (GD Medal winner 2005) . • "Convergent incremental optimization transfer algorithms: application to tomography", S. Ahn, J.A. Fessler, D. Blatt, and A. Hero, IEEE Trans. on Medical Imaging, vol. 25, no. 3, pp.283-296, March 2006 4th Year ARO MURI Review June, 2006

  30. Pubs Since June 2005 • Conference • "Sequential Design of Experiments for a Rayleigh Inverse Scattering Problem," R. Rangarajan, R. Raich, and A.O. Hero, Proc. Of IEEE Workshop on Statistical Signal Processing (SSP), Bordeaux, July 2005. • "APOCS: a convergent source localization algorithm for sensor networks," D. Blatt and A.O. Hero, IEEE Workshop on Statistical Signal Processing (SSP), Bordeaux, July 2006 • "Incremental optimization transfer algorithms: application to transmission tomography", S. Ahn, J.A. Fessler, D. Blatt, and A. Hero, IEEE Conf on Medical Imaging, Oct. 2005. • "A Comparison of Task Driven and Information Driven Sensor Management for Target Tracking," C. Kreucher, A. Hero, and K. Kastella, 44th IEEE Conference on Decision and Control (CDC) Special Session on Information Theoretic Methods for Target Tracking, December 12-15 (Invited) • "Single-stage waveform selection for adaptive resource constrained state estimation," R. Raghuram, R. Raich and A.O. Hero, IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Toulouse France, June 2006. • "Optimal sensor scheduling via classification reduction of policy search (CROPS)," D. Blatt and A.O. Hero, 2006 Workshop on POMDP's, Classification and Regression (Intl Conf on Automated Planning and Scheduling (ICAPS)), Cumbria UK, June 2006. (Invited) 4th Year ARO MURI Review June, 2006

  31. Synergistic Activities and Awards since June 2005 • Sensip Nov 2005, A. Hero plenary speaker • Member of ARO MURI (John Sidles PI) awarded in 2005 for MRFM sensing and image reconstruction • Member of AFOSR MURI (Randy Moses PI) awarded in 2006 for multi-platform radar sensing • Member of ISP team (Harry Schmit PI) • General Dynamics, Inc • K. Kastella: collaboration with A. Hero in sensor management, July 2002- • C. Kreucher: former doctoral student of A. Hero, continued collaboration • M. Moreland Melbourne: collaborator in area of sensor management • Ben Shapo: MS student collaborator in area of sensor management • Mike Davis: MS student collaborator in area of satellite MIMO • ARL • NRC ARLTAB: A Hero is member of NAS oversight/review committee • ARLTAB SEDD: A. Hero participated in yearly review • Night Vision Lab: Jay Marble spent two weeks of Aug 2005 with Steve Bishop • EIC of Foundations and Applications of Sensor Management (Springer - 2006) • Contributor, IEEE Proceeedings Special Issue on Large Scale Complex Systems, Editor S. Haykin. 4th Year ARO MURI Review June, 2006

  32. Synergistic Activities (ctd) • In May 2005 UM Student Jay Marble was at Georgia Tech (working with Waymond Scott) • In Aug 2005 Jay Marble was at Night Vision Lab (working with Steve Bishop) : • Indirectly support the Autonomous Mine Detection System (AMDS) • Identify new data sets for algorithm validation: “Check Test 1” (April 2005) • Apply multi-stage reinforcement learning algorithms to Army problems. • Further develop demonstration software for illustrating algorithm performance. • A. Hero visited AFRL Rome (B. Bonneau) in Nov. and gave invited presentation at Sensip on sensor management and at the “Old Crows Conference” at AFRL. • Collaboration with Eric Michielsson on IRIS started in fall 2005 – led to several proposals to DARPA, ARO. 4th Year ARO MURI Review June, 2006

  33. Transitions • Transition of SM methods to control of sensor swarms (GD) – resulted in GD sensor net demo. • Marble visited NVRL for 1 month in summer 2005 to demo UM mine detection software • June 2006 Marble is now full-time employee at Night Vision Research Laboratory 4th Year ARO MURI Review June, 2006

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