Adaptive CFAR Detection for Clutter-Edge Heterogeneity Using Bayesian Inference

Biao Chen, Pramod K. Varshney, James H. Michels

Research output: Contribution to journalArticle

25 Scopus citations

Abstract

Radar constant false alarm rate (CFAR) detection is addressed in this correspondence. Motivated by the frequently encountered problem of clutter-edge heterogeneity, we model the secondary data as a probability mixture and impose a hierarchical model for the inference problem. A two-stage CFAR detector stucture is proposed. Empirical Bayesian inference is adopted in the first stage for training data selection followed by a CFAR processor using the identified homogeneous training set for target detection. One of the advantages of the proposed algorithm is its inherent adaptivity; i.e., the threshold setting is much less sensitive to the nonstationary environment compared with other standard CFAR procedures.

Original languageEnglish (US)
Pages (from-to)1462-1470
Number of pages9
JournalIEEE Transactions on Aerospace and Electronic Systems
Volume39
Issue number4
DOIs
StatePublished - Oct 1 2003

ASJC Scopus subject areas

  • Aerospace Engineering
  • Electrical and Electronic Engineering

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