Convergence Performance of Adaptive Detectors. Part 1

Abstract

Convergence results for a mean level adaptive detector (MLAD) are presented. The MLAD consists of an adaptive matched filter (for spatially correlated inputs) followed by a mean level detector (MLD). The optimal weights of the adaptive matched filter are estimated from one batch of ata and applied to a statistically independent batch of nonconcurrent data. The threshold of the MLD is determined from the resultant data. Thereafter, a candidate cell is compared against this threshold. Probabilities of false alarm and detection are derived as functions of the threshold factor, the order of the matched filter, the number of independent samples per channel used to calculate the adaptive matched filter weights, the number of samples used to set the MLD threshold, and the output signal-to-noise power ratio of the optimal matched filter. A number of performance curves are shown and discussed.

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Document Details

Document Type
Technical Report
Publication Date
Jul 30, 1991
Accession Number
ADA239507

Entities

People

  • Karl R. Gerlach

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

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

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Convergence
  • Covariance
  • Data Science
  • Detection
  • Detectors
  • False Alarms
  • Filters
  • Gaussian Processes
  • Information Science
  • Matched Filters
  • Military Research
  • Noise
  • Probability
  • Random Variables
  • Warning Systems

Fields of Study

  • Engineering

Readers

  • Neural Network Machine Learning.
  • Operations Research
  • Radio communications and signal processing.