Conjugate Gradient Parametric Detection of Multichannel Signals (Preprint)

Abstract

The parametric adaptive matched filter (PAMF) detector for space-time adaptive processing (STAP) detection is re-examined in this paper. Originally, the PAMF detector was introduced by using a multichannel autoregressive (AR) parametric model for the disturbance signal in STAP detection. While the parametric approach brings in benefits such as reduced training and computational requirements as compared with fully adaptive STAP detectors, the PAMF detector as a reduced-dimensional solution remains unclear. This paper employs the conjugate-gradient (CG) algorithm to solve the linear prediction problem arising in the PAMF detector. It is shown that CG yields not only a new computationally efficient implementation of the PAMF detector, a new and efficient AR model order selection method that can naturally be integrated with CG iterations, but it also offers new perspectives of PAMF as a reduced-rank subspace detector.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2012
Accession Number
ADA562236

Entities

People

  • Chaoshu Jiang
  • Hongbin Li
  • Muralidhar (Murali) Rangaswamy

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Computational Complexity
  • Detection
  • Detectors
  • Engineering
  • Estimators
  • Governments
  • Iterations
  • Layered Sensing
  • Multichannel
  • Probability
  • Radio Frequency
  • Signal Detection
  • Simulations
  • Vector Spaces

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.

Technology Areas

  • Space
  • Space - Space Objects