Detection Performance of Generalized Likelihood Ratio Processors for Random Signals of Unknown Location, Structure, Extent, and Strength

Abstract

A signal (if present) is located somewhere in a band of frequencies characterized by a total of N search bins. The signal occupies an arbitrary set of M of these bins, where not only is M unknown, but also, the locations of the particular M occupied bins are unknown. Also, the signal strength is unknown. The generalized likelihood ratio (GLR) method furnishes an approach for estimating all the unknown parameters and for testing for presence or absence of the signal in an observation of the outputs of the N search bins. However, there is nothing guaranteed optimum about the GLR approach. Also, the optimum (likelihood ratio) processor cannot be constructed or realized, due to all the unknowns and the voluminous amount of searching required for this scenario. These deleterious conditions force adoption of some suboptimum processing techniques, guided by the GLR and likelihood ratio results. Detection, Likelihood ratio, Generalized Likelihood, Unknown Location, Unknown structure, Unknown Extent, Unknown strength, Maximum likelihood, False alarm probability, Detection probability, Order statistics, Characteristic function, Simulation.

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Document Details

Document Type
Technical Report
Publication Date
Aug 25, 1994
Accession Number
ADA285589

Entities

People

  • Albert H. Nuttall

Organizations

  • Naval Undersea Warfare Center

Tags

Communities of Interest

  • Energy and Power Technologies
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Detection
  • Distribution Functions
  • False Alarms
  • Frequency
  • Information Science
  • Integrals
  • Military Research
  • Observation
  • Plastic Explosives
  • Probability
  • Probability Density Functions
  • Random Variables
  • Signal Processing
  • Statistics
  • Undersea Warfare
  • Warfare

Fields of Study

  • Engineering

Readers

  • Applied Combinatorial Optimization and Logic Circuit Design.
  • Radio communications and signal processing.
  • Regression Analysis.