Classification VIA Information-Theoretic Fusion of Vector-Magnetic and Acoustic Sensor Data

Abstract

We present a general approach for multi-modal sensor fusion based on nonparametric probability density estimation and maximization of a mutual information criterion. We apply this approach to fusion of vector-magnetic and acoustic data for classification of vehicles. Linear features are used, although the approach may be applied more generally with other sensor modalities, nonlinear features, and other classification targets. For the magnetic data, we present a parametric model with computationally efficient parameter estimation. Experimental results are provided illustrating the effectiveness of a classifier that discriminates between cars and sport utility vehicles.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2007
Accession Number
ADA489841

Entities

People

  • Brian M. Sadler
  • Richard J. Kozick

Organizations

  • Bucknell University

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Acoustics
  • Algorithms
  • Classification
  • Data Science
  • Detectors
  • Feature Extraction
  • Machine Learning
  • Magnetic Detectors
  • Magnetic Dipoles
  • Magnetic Fields
  • Magnetic Properties
  • Magnetometers
  • Probability
  • Random Variables
  • Sensor Networks
  • Signal Processing
  • Vehicles

Readers

  • Neural Network Machine Learning.
  • Sensor Fusion and Tracking Systems.