Cutset Sampling Topologirs for Intelligence

Abstract

To develop novel constraint-aware approaches for designing dynamic structures and distributed algorithms to be used in missions involving geospatial data gathering tasks. The proposed approaches will seamlessly integrate the management of: (1)spatial sensor deployment and data communication constraints, (2) signal characteristics (e.g.,continuity, expected size and shape), and (3) unconventional tradeoffs in terms of balancinginformation gains with spatiotemporal constraints. We aim at achieving accurate and robust signal reconstruction, interpolation, and analysis for the purpose of information extraction. The proposed technology will significantly enhance naval capabilities for remote detection and assessment of various security threats, spanning from motion of actual physical entities through weapons of mass destruction.

Document Details

Document Type
DoD Grant Award
Publication Date
Sep 21, 2018
Source ID
N000141712707

Entities

People

  • Thrasyvoulos N. Pappas

Organizations

  • Northwestern University
  • Office of Naval Research
  • United States Navy

Tags

Readers

  • Distributed Systems and Data Platform Development
  • Geospatial Intelligence and Artificial Intelligence Analytics
  • Image Processing and Computer Vision.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - DoD AI Strategy