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Sequential Adaptive Multi-Modality Target Detec-tion and Classification using Physics-Based Models

Sequential Adaptive Multi-Modality Target Detec-tion and Classification using Physics-Based Models. Professor Andrew E. Yagle (PI) (EECS) Mine detection, channel identification Professor Alfred O. Hero III (EECS) Sensor scheduling, nonparametric statistical models

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Sequential Adaptive Multi-Modality Target Detec-tion and Classification using Physics-Based Models

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  1. Sequential Adaptive Multi-Modality Target Detec-tion and Classification using Physics-Based Models • Professor Andrew E. Yagle (PI) (EECS) Mine detection, channel identification • Professor Alfred O. Hero III (EECS) Sensor scheduling, nonparametric statistical models • Professor Kamal Sarabandi (Director, Rad Lab) Vehicle and foliage physics-based modelling • Assistant Professor Marcin Bownik (Mathematics) Basis functions and mathematical modelling

  2. Sequential Adaptive Multi-Modality Target Detec-tion and Classification using Physics-Based Models • Professor Andrew E. Yagle Jay Marble, Siddharth Shah • Professor Alfred O. Hero III Chris Kreucher, Doron Blatt, Jose Costa, Neal Patwari, Raghuram Rangarajan, Krishnakanth Subramanian, Mike Fitzgibbons, Cyrille Hory • Professor Kamal Sarabandi Mark Casciato, Il-Suek Koh, M. Dehmolaian

  3. Sequential Adaptive Multi-Modality Target Detec-tion and Classification using Physics-Based Models PROJECT SUPERVISION: • Dr. Douglas Cochran (DARPA) • Dr. Russell Harmon (ARO) INDUSTRY COLLABORATION: • Veridian (formerly ERIM) of Ann Arbor

  4. Sequential Adaptive Multi-Modality Target Detec-tion and Classification using Physics-Based Models • Mine detection: Yagle, Marble • Vehicle modeling: Sarabandi, Casciato • Foliage modeling: Sarabandi, Koh, Dehmolaian • Sensor scheduling: Hero, Kreucher • Nonparametric statistics: Hero, Blatt • Distributed detection: Hero, Patwari • Basis functions: Yagle, Bownik

  5. HERO: Accomplishments • Developed non-parametric statistical modelling using MRFs for target+clutter vs. clutter • Developed target model reduction technique • Developed distributed multisensor detection using hierarchical sensor aggregation • Developed myopic sequential adaptive sensor management for tracking

  6. Sarabandi: Accomplishments • Performed phenomenological studies of: • (a) physics-based clutter models • (b) physics-based target models • Developed SAR/INSAR image simulator • Developed time-reversal method for foliage camouflaged target detection • Developed iterative frequency-correlation-based forest radar channel identification

  7. YAGLE: Accomplishments • Developed mine detection algorithm from SAR using range migration imaging (with Jay Marble) • Developed 2-D and3-D blind deconvolution algs for radar channel identification (with Siddharth Shah) • Developing basis-function-based inverse scattering approach (work in progress with Marcin Bownik)

  8. Synergistic Activities: Hero VERIDIAN INT’L, Ann Arbor: C. Kreucher: sensor management & scheduling K. Kastella: sensor management J. Ackenhusen: mine detection ARL: NAS-SED review panel member N. Patwari (student) summer internship ERIM: B. Thelen, N. Subotic collaborators

  9. Synergistic Activities: Sarabandi VERIDIAN: John Ackenhusen BAE: Norm Byer FCS COMMUNICATIONS: Jim Freibersiser (DARPA PM) Barry Perlman (CECOM) ARL: Ed Burke (mm wave), Brian Sadler, Bruce Wallace

  10. Synergistic Activities: Yagle VERIDIAN INT’L, Ann Arbor, MI: Jay Marble, student (ARO mine research) Brian Fischer, student (Low RCS material design) Chris Wackerman, former Ph.D. student

  11. Research Project Objectives • Develop overall algorithm for detection of: Tanks under trees; landmines • Initial focus:TUT (can hit the ground running) • Features of algorithm: sequential detection, sensor management & selection, physics-based models • Simplify stochastic physics-based models using: functional-analysis-based approximation • Evaluate the resulting procedure on realistic models (statistical simulations) and real data

  12. Issues: Overall Algorithm • How to select sensing modalities? • What is value-added for combining other modalities? Is it worth additional cost? • How do we implement data-adaptive configu-rations, e.g., selection of sources/receivers, based on scattering of targets and propagation in medium? • What are the figures of merit? • How to select decision thresholds?

  13. Physics based models

  14. Summary • Sequential detection and classification • Sensor scheduling and management • Physics-based models with dimensionality reduced using functional analysis • Vehicle and canopy scattering models already at UM permit test evaluations

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