Integrated Multi-Modal RF Sensing. Sensor Integration and Joint PDF Construction for Distributed Detection and Classification

Abstract

With multiple sensors in distributed systems, one is expected to make better decisions than with a single sensor. We investigate the problem of sensor integration to combine all the available information. In this paper, we propose a novel method of constructing the joint probability density function (PDF) based on the exponential family. This method does not require the knowledge of the marginal PDFs and hence is useful in many practical cases. We prove that our method is asymptotically optimal in Kullback-Leibler (KL) divergence. It is shown that the performance of our method is the same as existing methods, while it requires less information.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2011
Accession Number
ADA545146

Entities

People

  • Muralidhar (Murali) Rangaswamy
  • Quan Ding
  • Steven Kay

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Biomedical
  • Sensors

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Artificial Intelligence
  • Classification
  • Construction
  • Data Science
  • Detection
  • Detectors
  • Gaussian Noise
  • Government Procurement
  • Information Science
  • Machine Learning
  • Military Research
  • Probability
  • Probability Density Functions
  • Rhode Island
  • Statistics

Fields of Study

  • Computer science
  • Engineering

Readers

  • Sensor Fusion and Tracking Systems.
  • Statistical inference.