On Hypothesis Testing in Distributed Sensor Networks.

Abstract

In this report, some hypothesis testing problems in distributed sensor networks are considered. Optimum data fusion rules are obtained when the decision rules at the detectors are known. The distributed hypothesis testing problem with a distributed data fusion is solved using the Bayesian as well as the Neyman-Pearson approach. The decentralized Neyman-Pearson hypothesis testing problem and the sequential hypothesis testing problem for a tandem topology network are investigated. The distributed sequential probability ratio test problem is also studied. In all these problems, optimal strategies at each site and at each time stage are obtained. Keywords include: Fusion, Surveillance, Remote Receivers, Detection, and Estimation. (r.h.)

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1987
Accession Number
ADA195910

Entities

People

  • Pramod Varshney
  • Zelneddine Chair

Organizations

  • Syracuse University

Tags

Communities of Interest

  • Human Systems
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Data Fusion
  • Decision Theory
  • Detection
  • Detectors
  • Dynamic Programming
  • Equations
  • False Alarms
  • Information Science
  • Probability
  • Random Variables
  • Sensor Networks
  • Signal Detection
  • Stochastic Processes
  • Topology
  • Wireless Sensor Networks

Readers

  • Operations Research
  • Regression Analysis.
  • Sensor Fusion and Tracking Systems.

Technology Areas

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