Nguyen - Sensing, Surveillance and Navigation - Spring Review 2013


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Dr. Tristan Nguyen presents an overview of his program, Sensing, Surveillance and Navigation, at the AFOSR 2013 Spring Review. At this review, Program Officers from AFOSR Technical Divisions will present briefings that highlight basic research programs beneficial to the Air Force.

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Nguyen - Sensing, Surveillance and Navigation - Spring Review 2013

  1. 1. 1DISTRIBUTION STATEMENT A – Unclassified, Unlimited Distribution15 February 2013 Integrity  Service  Excellence Tristan Nguyen Program Officer (Acting) AFOSR/RTC Air Force Research Laboratory Sensing, Surveillance, and Navigation Date: 06 03 2013
  2. 2. 2DISTRIBUTION STATEMENT A – Unclassified, Unlimited Distribution 2013 AFOSR SPRING REVIEW NAME: Tristan Nguyen BRIEF DESCRIPTION OF PORTFOLIO:  Funding basic research to support integrated RF sensing and surveillance or navigation in possibly GPS-denied environments.  Common Challenges: cluttered scene; unknown moving targets; unknown terrains; unpredictable environments; multiple (changing) objectives. LIST SUB-AREAS IN PORTFOLIO:  Waveform Design & Fully Adaptive Radar  Radar Imaging  Target Identification through Imaging  Non GPS-based Navigation To achieve robust, mobile sensing & communications To enable unimpeded surveillance
  3. 3. 3DISTRIBUTION STATEMENT A – Unclassified, Unlimited Distribution 2013 AFOSR SPRING REVIEW Connection with other AFOSR Programs: Sensing, Surveillance, Navigation Information Fusion Complex Networks Signaling for RF communications Networked communications modeling and more Imagery data; close to sensors (right before or after) Diverse data sources; away from sensors Network data Technical Tools: Synthesis of ideas in  Physics – EM scatterings; ionospheric scintillation  Electrical engineering – Coding; signal analysis or synthesis  Mathematics – Fourier integral operators; geometry of wave fronts  Statistics – Estimation-Detection theory; information theory
  4. 4. 4DISTRIBUTION STATEMENT A – Unclassified, Unlimited Distribution Integration of Waveform Design and Fully Adaptive Radar Conceptual Design  Two-block optimization paradigm between waveform generation (Transmit Resource) and channel assessment (Receiver).  New design of diverse transmit sequences, modulation, filtering and decision rules.  New formulation of Mutual Information to significantly improve target detection under cluttered conditions.
  5. 5. 5DISTRIBUTION STATEMENT A – Unclassified, Unlimited Distribution New RF Imaging Modalities  Evolution of SAR Imaging • Mono-static to Multi-static SAR • (Fully) Passive SAR* • Inverse SAR (ISAR) • Interferometric SAR (IfSAR or InSAR)* • Polarimetric SAR (PolSAR) • Ultra-narrowband SAR* • 3D SAR*  Imaging with Sparse Distributed Apertures  Tomographic Imaging with Active Noise * Motivated by Fourier Integrator Operators
  6. 6. 6DISTRIBUTION STATEMENT A – Unclassified, Unlimited Distribution Target Identification with Invariants Some Research Questions:  Finding weaker notions of invariance that live on the image space  Characterizing the image space and finding its properties
  7. 7. 7DISTRIBUTION STATEMENT A – Unclassified, Unlimited Distribution Non GPS-based Tracking & Navigation Integrating Adaptive Sensing with Inference for Navigation and Surveillance  Multiple RF sensing modalities are desired to support navigation  RF imaging is key in guidance and navigation  Inference of obstructions and opportunistic targets is required  Cooperative sensing is required for tracking.
  8. 8. 8DISTRIBUTION STATEMENT A – Unclassified, Unlimited Distribution Retain:  Multi-Antenna “Diverse Transmit” and Distributed Sensing  Geometric Invariant Analysis of Images  Mathematical Innovation in Passive and Opportunistic Radar  Surveillance through Coupling of Viewing and Navigating Functions De-emphasize:  Classical Pattern-matching in Target Identification  Satellite Resource Optimization  Dual-frequency Spatial Light Modulator Adaptive Optics Program’s Future Direction
  9. 9. 9DISTRIBUTION STATEMENT A – Unclassified, Unlimited Distribution Other DoD Programs ARO emphasizes Opportunistic Sensing but without RF imaging. PM: Liyi Dai ONR emphasizes: image understanding (EO); acoustics-based communications, imaging, and target recognition in the underwater environment. PMs: Behzad Kamgar-Parsi; John Tague DARPA: Mathematics of Sensing, Exploitation, and Execution (MSEE) emphasizes video, imagery data and not imaging techniques. PM: Anthony Falcone Mind’s Eyes emphasizes visual intelligence by combining machine vision and reasoning. PM: James Donlon Information in a Photon (InPho) emphasizes quantum optical imaging against stealth targets, PM: Mari Maeda
  10. 10. 10DISTRIBUTION STATEMENT A – Unclassified, Unlimited Distribution Collaborations & Transitions AFRL/RY with academic PIs – M. Rangaswamy with R. Narayanan (Penn State U.) and S. Kay (U. Rhode Island)  Joint publications  Recruitment of a former summer intern at AFRL who will be employed after completion of Ph.D. at UC Irvine. International Efforts:  DRDC Ottawa (Noise Radar)  Singapore Nanyang Technical University (INS and Integrated Geo-location/Timekeeping)
  11. 11. 11DISTRIBUTION STATEMENT A – Unclassified, Unlimited Distribution Highlights of Projects
  12. 12. 12DISTRIBUTION STATEMENT A – Unclassified, Unlimited Distribution Bistatic SAR Imaging Using Ultra-Narrowband B. Yazici et al., Rensselaer Polytechnic Institute Problem: New imaging modality using high Doppler resolution. How:  Construct a forward model for imaging by correlating received signal with scaled transmitted signal (Fourier Integral Operator = FIO).  Compute iso-Doppler surface.  Reconstruct scene reflectivity from iso-Doppler contours via “inverting” the FIO. New Capabilities:  Arbitrary ground topography & flight trajectories  Passive imaging works as well  Preserve edges in images  Image is analytically and efficiently constructed.
  13. 13. 13DISTRIBUTION STATEMENT A – Unclassified, Unlimited Distribution Signal Processing via Random Matrix Theory R. Nadakuditi et al., University of Michigan – Anne Arbor Problem: Formulate random-matrix theoretic approaches to sensor fusion in the low SNR regime How:  Define eigen-statistics on random matrix.  Study subspaces corresponding to subsets of eigen-spectrum of random matrix. Preliminary Findings:  Dimension reduction should be guided by techniques from random matrix theory.  Adding more sensors can degrade the detection performance when SNR is low.
  14. 14. 14DISTRIBUTION STATEMENT A – Unclassified, Unlimited Distribution Cognitive Radar Network for Multiple-Target Tracking A. Nehorai et al., Washington University – St. Louis Problem: Tracking multiple targets in urban environment with cognitive radars. How to choose the best subset of radars and power allocated to them at each time? Challenges: multipath; high- dimensional state vectors (channel state and targets’ kinematic states) How:  Construct a Rao-Blackwell particle filter (to approximate sequential Bayesian filter) for tracking  Use the posterior Cramer-Rao bound to schedule radars and optimize power allocation. Simulation
  15. 15. 15DISTRIBUTION STATEMENT A – Unclassified, Unlimited Distribution Tree-based Context Model for Object Recognition A. Willsky et al., MIT This work complements the PI’s theoretical research in Gaussian Graphical Models. Problem: Detect and localize multiple objects in natural images. Recognition of multiple objects in images is very difficult. How:  Construct a prior tree-based model that encodes co-occurrence statistics and spatial relationships between objects.  Integrate global image features and local detector’s outputs  Learn the tree structure from an image database. Result: Significant improvement over the baseline local detector.
  16. 16. 16DISTRIBUTION STATEMENT A – Unclassified, Unlimited Distribution Estimation of Ionospheric Scintillation Effects J. Morton et al., Miami University Problem: Use signal processing techniques to detect the plasma drift velocity using GPS carrier phase measurements. Challenges: multipath; clock errors; dynamics How:  Remove unwanted these effects using a DWT.  Find an accurate time-frequency method to generate high-resolution power spectrum of the signal received from multiple (3) antennas.  Determine the time differences between scintillation events from the cut-off frequencies on the power spectrum plot.  Use antennas’ locations with the time differences to determine the plasma drift velocity.
  17. 17. 17DISTRIBUTION STATEMENT A – Unclassified, Unlimited Distribution Some LRIR Tasks  Charles Matson: An information-theoretic investigation of blind deconvolution image processing  Sengveing Amphay: Azimuth-Scanning synthetic Aperture Radar: Signal Processing, Imaging and Data Collection Strategies  Keith Knox: Improved SSA Imaging by the Application of Compressive Sensing Techniques  Daniel Stevens: Characterization of the Method of Time-Frequency Reassignment  David Hughes: Measuring Quantum Data Encrypted Modulation States  Muralidhar Rangaswamy: Waveform Design, Optimization and Adaptive Processing Techniques for multiple input-multiple output (MIMO) Radar and Fully Adaptive Radar (FAR)  Braham, Himed: Radar Waveform Optimization
  18. 18. 18DISTRIBUTION STATEMENT A – Unclassified, Unlimited Distribution Sensing, Surveillance, and Navigation Conclusion  New concepts are needed to closely integrate, at least, some of the key aspects of sensing, detection, and navigation.  New technical tools in physics, engineering, mathematics, and statistics are also needed to formalize these concepts.
  19. 19. 19DISTRIBUTION STATEMENT A – Unclassified, Unlimited Distribution Sensing, Surveillance, and Navigation