Asynchronous Data-Driven Classification of Weapon Systems

Abstract

This paper addresses real-time weapon classification by analysis of asynchronous acoustic data, collected from microphones on a sensor network. The weapon classification algorithm consists of two parts: (i) feature extraction from time-series data using Symbolic Dynamic Filtering (SDF), and (ii) pattern classification based on the extracted features using Language Measure (LM) and Support Vector Machine (SVM). The proposed algorithm has been tested on field data, generated by firing of two types of rifles. The results of analysis demonstrate high accuracy and fast execution of the pattern classification algorithm with low memory requirements. Potential applications include simultaneous shooter localization and weapon classification with soldier-wearable networked sensors.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2009
Accession Number
ADA507967

Entities

People

  • Asok Ray
  • Kushal Mukherjee
  • Shalabh Gupta
  • Shashi Phoha
  • Thyagaraju Damarla
  • Xin Jin

Organizations

  • Pennsylvania State University

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Acoustic Signals
  • Classification
  • Data Analysis
  • Data Sets
  • Detectors
  • Feature Extraction
  • Language
  • Machine Learning
  • Military Research
  • Probability
  • Probability Distributions
  • Sensor Networks
  • Shock Waves
  • Supervised Machine Learning
  • Target Recognition
  • Weapon Systems
  • Weapons

Fields of Study

  • Computer science

Readers

  • Computer Science/Computer Engineering/Data Science/Digital Signal Processing.
  • Neural Network Machine Learning.
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML