Bayesian radar detection in interference

Pu Wang, Hongbin Li, Braham Himed

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Scopus citations

Abstract

This chapter aims to discuss recent advances on Bayesian KA-STAP techniques. It unfolds as the classical STAP signal model in Section 5.2 evolves into a framework of KA-STAP model including a knowledge-aided homogeneous model, a knowledge-aided partially homogeneous model, and a knowledge-aided compound Gaussian model in Section 5.3. Then in Section 5.4, a hierarchical two-layered STAP model is discussed, which provides a new way to describe the non-homogeneity between the test and training data. Section 5.5 is devoted to parametric Bayesian detectors that integrate structural space-time information, i.e., a multi-channel autoregressive (AR) process, for the interference model and the consequent Bayesian estimation. The resulting Bayesian parametric detectors allow a fast implementation and further reduction in the amount of training data needed for reliable detection.

Original languageEnglish
Title of host publicationModern Radar Detection Theory
Pages133-164
Number of pages32
ISBN (Electronic)9781613532003
DOIs
StatePublished - 1 Jan 2016

Keywords

  • AR process
  • Autoregressive processes
  • Bayes methods
  • Bayesian KA-STAP technique
  • Bayesian estimation
  • Bayesian radar detection
  • Classical STAP signal model
  • Estimation theory
  • Hierarchical two-layered STAP model
  • Interference model
  • Knowledge-aided compound Gaussian model
  • Knowledge-aided partially homogeneous model
  • Multichannel autoregressive process
  • Parameter estimation
  • Parametric Bayesian detector
  • Radar detection
  • Radar interference
  • Space-time adaptive processing
  • Structural space-time information

Fingerprint

Dive into the research topics of 'Bayesian radar detection in interference'. Together they form a unique fingerprint.

Cite this