Infrasonic Cyber-Physical Masint for Environmental Characteristics and Classification

Abstract

This report was developed under a Cooperative Agreement. This work developed an advanced machine learning (ML) technique that is uniquely capable of exploiting heterogeneous and potentially lower-fidelity, cyber-physical signatures. In this effort, we moved beyond traditional collection and analysis of these signatures, instead utilized mobile devices to capture infrasonic signatures. These distributed mobile devices can be used to collect low frequency sound waves from targets of interest, such as small to medium UAVs, artillery, and other targets of interest. Report presents classification results for some targets of interest.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2021
Accession Number
AD1126590

Entities

People

  • Adrian Peter
  • Anand Rangarajan
  • Anthony O. Smith
  • Milton A. Garces

Organizations

  • Florida Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • Cyber
  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automata Theory
  • Computational Science
  • Computer Languages
  • Computer Programming
  • Computers
  • Data Mining
  • Dimensionality Reduction
  • Feature Extraction
  • Information Retrieval
  • Information Science
  • Machine Learning
  • Network Science
  • Neural Networks
  • Processing Equipment
  • Supervised Machine Learning
  • Unmanned Aerial Vehicles

Fields of Study

  • Computer science

Readers

  • Distributed Systems and Data Platform Development
  • Seismology

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Cyber