Optimization Techniques for Network Security, Distributed Agents, and RF Sensor Coexistence

Abstract

A primary objective of the US Army Research Laboratory (ARL) is to bring together the scientific and military communities through collaboration on research that will directly benefit the Warfighter. A wide array of projects that ARL supports involves optimization of a noise-corrupted loss function over a potentially high-dimensional parameter space. In this report, we detail three areas of research that ARL is involved in that may benefit from stochastic optimization: adversarial machine learning, distributed agents, and spectrum sensing for radar.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2018
Accession Number
AD1064914

Entities

People

  • Anthony F. Martone
  • Gunjan Verma
  • Michael J. Weisman
  • Robert J. Drost

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Advanced Electronics
  • Autonomy
  • Energy and Power Technologies
  • Engineered Resilient Systems
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Neural Networks
  • Bandwidth
  • Channel Models
  • Communication Channels
  • Computational Complexity
  • Diffraction
  • Frequency
  • Frequency Bands
  • Information Science
  • Learning
  • Lepidoptera
  • Line Of Sight
  • Machine Learning
  • Military Research
  • Multiobjective Optimization
  • Neural Networks
  • Optimization
  • Power Spectra
  • Radio Frequency
  • Radio Frequency Interference
  • Scattering
  • Spectra
  • Urban Areas

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Defense Technology Research and Development.
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Cyber
  • Fully Networked C3
  • Fully Networked C3 - Command and Control
  • Space