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Published on January 14, 2008

Author: Sibilla

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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 Professor Kamal Sarabandi (Director, Rad Lab) Vehicle and foliage physics-based modelling Assistant Professor Marcin Bownik (Mathematics) Basis functions and mathematical modelling 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 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 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 PROJECT SUPERVISION: Dr. Douglas Cochran (DARPA) Dr. Russell Harmon (ARO) INDUSTRY COLLABORATION: Veridian (formerly ERIM) of Ann Arbor 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 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 HERO: Accomplishments:  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 Sarabandi: Accomplishments:  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 YAGLE: Accomplishments:  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) Synergistic Activities: Hero:  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 Synergistic Activities: Sarabandi:  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 Synergistic Activities: Yagle:  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 Research Project Objectives:  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 Issues: Overall Algorithm:  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? Summary:  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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