Gaussian Acoustic Classifier for the Launch of Three Weapon Systems

Abstract

The U.S. Army is interested in locating and classifying hostile weapons fire to improve the Soldiers real-time situational awareness. Acoustic localization systems such as the Unattended Transient Acoustic MASINT System (UTAMS) have been demonstrated in theater. However, developing a classifier algorithm is a difficult problem due to atmospheric and propagation effects as well as acoustic interference and noise. Techniques were developed to accurately classify acoustic weapons system fire. Robust features were calculated in the time domain and used to train a Gaussian classifier. The algorithm was tested and trained using data collected in 2005, 2006, and 2011. The performance of the algorithm was similar to the results obtained by other researchers, but with significantly less computational complexity.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2013
Accession Number
ADA585973

Entities

People

  • Christine Yang
  • Geoffrey H. Goldman

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Acoustic Signatures
  • Algorithms
  • Amplitude
  • Classification
  • Data Science
  • Decision Theory
  • Department Of Defense
  • Information Science
  • Machine Learning
  • Military Research
  • Numbers
  • Situational Awareness
  • Square Roots
  • Supervised Machine Learning
  • Time Domain
  • Weapon Systems
  • Weapons

Readers

  • Sensor Fusion and Tracking Systems.
  • Wave Propagation and Nonlinear Chaotic Dynamics.