Knowledge-Based Generalized Likelihood Ratio Test (KB-GLRT): Exploiting Knowledge of the Clutter Ridge in Airborne Radar

Abstract

In this paper we address knowledge-based radar detection for STAP applications. To this end we exploit, at the design stage, the characteristic structure of the clutter ridge and devise two decision rules according to the Generalized Likelihood Ratio Test (GLRT) and the two-step GLRT criteria. We first focus on the case of a clutter ridge with integer slope and then discuss the more general framework of a non-integer slope parameter. With reference to this last case we only provide approximate GLRT detectors due to the analytical difficulties connected with the exact solution of the problem. The performance analysis shows that the new knowledge-based systems achieve a performance level very close to the optimum detector which assumes the perfect knowledge of the clutter covariance matrix and can outperform some previously proposed adaptive detection schemes.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2004
Accession Number
ADA430052

Entities

People

  • Alfonso Farina
  • Antonio De Maio
  • Michael Wicks

Organizations

  • Rome Laboratory

Tags

Communities of Interest

  • Human Systems
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Airborne
  • Covariance
  • Data Science
  • Databases
  • Detection
  • Detectors
  • Filters
  • Frequency
  • Information Science
  • Knowledge Based Systems
  • Military Research
  • Monte Carlo Method
  • New York
  • Numbers
  • Statistics

Fields of Study

  • Engineering

Readers

  • Radar Systems Engineering.
  • Statistical inference.