Analysis of Parametric Adaptive Signal Detection with Applications to Radars and Hyperspectral Imaging

Abstract

New parametric space-time adaptive processing (STAP) based detectors are introduced and examined. Unlike conventional techniques that estimate the characteristics of the disturbance from only the secondary data, the proposed detectors obtain such knowledge jointly from the primary and secondary data. When the number of pulses within a coherent processing interval is sufficiently large, the proposed detectors can function even without any secondary data, making them strong candidates for detection in non-homogeneous environments. The proposed detectors are investigated by both theoretical analysis and numerical study using simulated and real radar data. Extensive comparison with conventional STAP methods shows that the proposed detectors can better deal with training-limited scenarios while being computationally simpler. The proposed techniques are also extended for target detection in hyperspectral imaging (HSI).

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 2010
Accession Number
ADA517361

Entities

People

  • Hongbin Li

Organizations

  • Stevens Institute of Technology

Tags

Communities of Interest

  • Biomedical
  • Sensors

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Computational Complexity
  • Computational Science
  • Computer Simulations
  • Data Sets
  • Detection
  • Detectors
  • Hyperspectral Imagery
  • Radar
  • Signal Detection
  • Signal Processing
  • Spectra
  • Target Detection
  • Two Dimensional
  • Warning Systems

Readers

  • Radar Systems Engineering.
  • Statistical inference.

Technology Areas

  • Space
  • Space - Space Objects