ALGORITHMS FOR SENSOR FUSION: Applications of Distance Measures and Probability of Error Bounds to Distributed. Detection Systems. Volume 2

Abstract

In this report, we consider some design and analysis aspects of distributed detection networks. The main focus is on the Bayesian approach to the design of these systems. They present a computationally efficient approach to the design of decentralized Bayesian detection systems. This procedure is based upon an alternate representation of the minimum average cost in terms of a modified form of the Kolmogorov variational distance. They demonstrate the utility of our approach by applying it to the design and performance evaluation of four decentralized detection structures.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1991
Accession Number
ADA254634

Entities

People

  • Pramod Varshney
  • Wael Hashlamoun

Organizations

  • Kaman Corporation

Tags

Communities of Interest

  • C4I
  • Human Systems
  • Sensors

DTIC Thesaurus Topics

  • Computational Science
  • Data Fusion
  • Detectors
  • Electrical Engineering
  • False Alarms
  • Information Processing
  • Network Topology
  • Probability Density Functions
  • Probability Distributions
  • Random Variables
  • Sensor Fusion
  • Sensor Networks
  • Signal Detection
  • Signal Processing
  • Statistics
  • Theses
  • Warning Systems

Readers

  • Neural Network Machine Learning.
  • Software Engineering

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms