Radar Signal Detection Based on Bayesian Hierarchical Models and Image Analysis Techniques

Abstract

The research undertaken under this effort has two major components. First the application of Bayesian inference theory is applied to problems ranging from Distributed Detection with multiple sensors clutter scene characterization/identification for airborne radar systems to adaptive CFAR detection with heterogeneous clutter. Secondly, multichannel radar detection algorithms are developed that are particularly suitable for airborne radar surveillance systems operating in a complex clutter/interference/noise environments.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
May 01, 2004
Accession Number
ADA423763

Entities

People

  • Biao Chen
  • Pramod K. Varsheny

Organizations

  • Syracuse University

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Bayesian Inference
  • Bayesian Networks
  • Data Science
  • Detection
  • Detectors
  • False Alarms
  • Information Science
  • Military Research
  • New York
  • Probability
  • Radar
  • Radar Signals
  • Signal Detection
  • Statistical Algorithms
  • Warning Systems

Fields of Study

  • Engineering

Readers

  • Distributed Systems and Data Platform Development
  • Radar Systems Engineering.

Technology Areas

  • AI & ML