Adaptive Detection in Subspaces

Abstract

This paper considers subspace based adaptive detection in the context of the likelihood ratio test studied by Kelly. The probability of false alarm for this test depends only on the subspace. Thus, we propose choosing the transformation onto the subspace to maximize the probability of detection over a likely class of noise and interference scenarios. An approximate solution to this optimization problem is described. This approach can lead to dramatic increases in the probability of detection given a fixed number of data observations due to a large gain in the statistical stability associated with the reduced dimension subspace. The relationship between subspace design for adaptive detection and partially adaptive beamformer design is explored. Simulations verify the analysis.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1990
Accession Number
ADA231023

Entities

People

  • Barry D. Van Veen
  • Chong H. Lee

Organizations

  • University of Wisconsin–Madison

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Analysis Of Variance
  • Background Noise
  • Covariance
  • Detection
  • Detectors
  • Environment
  • False Alarms
  • Noise
  • Observation
  • Optimization
  • Probability
  • Simulations
  • Simulators
  • Statistical Analysis
  • Warning Systems
  • White Noise

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Radar Systems Engineering.
  • Statistical inference.