Signal Detection in the Presence of Weakly Dependent Noise. Part II. Robust Detection.

Abstract

The problem of designing robust systems for detecting constant signals in the presence of weakly dependent noise with uncertain statistics is considered. A moving-average representation is used to model the dependence structure of the noise process, with the degree of dependence being parameterized by the averaging weights. Weak dependence is then modeled as the situation in which quantities depending to second or higher order on the averaging weights can be considered to be negligible. Uncertainty in the noise statistics is introduced within this framework by allowing a general type of uncertainty in the univariate statistics of the independent sequence that drives the moving average. To find robust detectors for signals in this type of weakly dependent noise environment, related results concerning robust location estimation in an analogous dependent situation are applied to modify a robust detection system for the corresponding independent-noise case.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1981
Accession Number
ADA124440

Entities

People

  • Vincent Poor

Organizations

  • University of Illinois Urbana–Champaign

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Data Science
  • Detection
  • Detectors
  • Distribution Functions
  • False Alarms
  • Information Science
  • Information Theory
  • Normal Distribution
  • Probability
  • Probability Distributions
  • Random Variables
  • Signal Detection
  • Statistical Inference
  • Statistics
  • United States
  • Warning Systems

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Plasma Physics / Magnetohydrodynamics
  • Statistical inference.