Information Fusion and Control in Hierarchical Systems

Abstract

We consider the distributed detection problem in trees with unbounded height. The first configuration we studied in this report is a balanced binary relay tree, where the leaves of the tree correspond to N identical and independent sensors. Only the leaves are sensors. The root of the tree represents a fusion center that makes the overall detection decision. Each of the other nodes in the tree are relay nodes that combine two binary messages to form a single output binary message. In this way, the information from the sensors is aggregated into the fusion center via the relay nodes. In Chapter II, we assume that the fusion rules are the unit-threshold likelihood-ratio test which are locally optimal in the sense of minimizing the total error probability after fusion. We describe the evolution of the Type I and Type II error probabilities of the binary data as it propagates from the leaves towards the root. Tight upper and lower bounds for the total error probability at the fusion center as functions of N are derived. These characterize how fast the total error probability converges to 0 with respect to N, even if the individual sensors have error probabilities that converge to 1/2.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2013
Accession Number
ADA584390

Entities

People

  • Ali Pezeshki
  • Edwin K. Chong
  • Stephen D. Howard
  • William Moran
  • Zhenliang Zhang

Organizations

  • Colorado State University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Detection
  • Detectors
  • Differential Equations
  • Differential Geometry
  • Geometry
  • Information Science
  • Information Theory
  • Neural Networks
  • Probability Density Functions
  • Probability Distributions
  • Random Variables
  • Sensor Networks
  • Signal Processing
  • Statistical Inference
  • Statistics
  • Target Tracking

Readers

  • Computer Networking
  • Mathematical Modeling and Probability Theory.
  • Regression Analysis.