COMBINING MACHINE LEARNING AND INFORMATION ELASTICITY PARADIGMS TO ENHANCE INTELLIGENT HETEROGENEOUS MULTI-SENSOR CROSS-DOMAIN DATA FUSION

Abstract

It is assumed that better effective decisions can be made in sensor processing if more “information” is available. However, this is not always the case since more information may not necessarily translate to better sensor detection performance. Elasticity of any differentiable function is the ratio of incremental change of the function with respect to the incremental change of its argument. Information elasticity of a sensor is defined as the ratio of the information’s usability or utility as an information metric. Machine learning techniques are being explored as a means to train sensor elastic performance to gain greater value.

Document Details

Document Type
DoD Grant Award
Publication Date
Aug 12, 2021
Source ID
FA95502010370

Entities

People

  • Ram M Narayanan

Organizations

  • Air Force Office of Scientific Research
  • Pennsylvania State University
  • United States Air Force

Tags

Fields of Study

  • Computer science
  • Engineering

Readers

  • Mechanical Engineering/Mechanics of Materials.
  • Neural Network Machine Learning.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks