Adaptive CFAR detection via Bayesian hierarchical model based parameter estimation

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

Research output: Contribution to journalConference article

2 Scopus citations

Abstract

Radar CFAR detection is addressed in this paper where the unknown noise/clutter statistics are modeled using a hierarchical structure. Considering the secondary data as a probability mixture due to the complex and heterogeneous background, parameter estimation is achieved using the empirical Bayesian approach. Unlike conventional cell averaging CFAR (and its variations) and order statistics CFAR, the new CFAR detection algorithm is less sensitive to the clutter edge location/duration. Performance evaluation is conducted via numerical simulation.

Original languageEnglish (US)
Pages (from-to)1396-1400
Number of pages5
JournalConference Record of the Asilomar Conference on Signals, Systems and Computers
Volume2
StatePublished - Dec 1 2001
Event35th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: Nov 4 2001Nov 7 2001

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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